First Author\[1\]<sup>\*</sup>, Second Author<sup>2</sup>  
(Double Blind Review: Please do not type or edit anything here until final-camera ready submissions)

<sup>1</sup>First affiliation, City and Country (Please do not type or edit anything here, our editors will do the work for you)

<sup>2</sup>Second affiliation, City and Country (Please do not type or edit anything here, our editors will do the work for you)

<table>
<tbody>
<tr class="odd">
<td>ARTICLE INFO</td>
<td></td>
<td>ABSTRACT</td>
</tr>
<tr class="even">
<td><p><em>Article history:</em></p>
<p>Received XX Month 2024</p>
<p>Revised XX Month 2024</p>
<p>Accepted XX Month 2024</p>
<p>Online first</p>
<p>Published 1 September 2024</p></td>
<td></td>
<td>Tourism Recommendation Systems (TRS) are increasingly important in the tourism industry to provide personalized recommendations based on diverse tourist preferences. Technology and big data have transformed TRS from traditional travel agencies to modern digital platforms, enabling the processing of vast amounts of user-generated data for precise recommendations. The study aims to identify strengths and weaknesses within existing TRS frameworks and techniques, propose recommendations to mitigate these weaknesses, and provide insights for practitioners and researchers. Key findings include recommendation algorithms, user modelling techniques, context awareness, and the integration of emerging technologies like artificial intelligence and machine learning. Future directions in TRS research should focus on exploring hybrid recommendation approaches, leveraging user-generated content and social media data, and developing intelligent systems capable of adapting to dynamic user preferences and contextual factors. This review contributes to a deeper understanding of contemporary TRS methodologies and provides actionable insights for enhancing TRS performance. By addressing current trends and proposing recommendations for future research, this paper aims to advance the field of TRS and improve travel experiences for tourists.</td>
</tr>
<tr class="odd">
<td><p><em>Keywords:</em></p>
<p>Tourism Recommendation Systems</p>
<p>Smart Tourism</p>
<p>Sustainable Tourism</p>
<p>Recommendation Systems</p>
<p>Travel Recommendation System</p>
<p><em>DOI:</em></p>
<p>10.24191/jcrinn.v9i2</p></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

# INTRODUCTION

The tourism sector has expanded significantly in recent years, and a wide range of in-person and online travel services are now offered. Tourists navigate complex destinations with unpredictable visit sequences and varying preferences. Effective tour planning involves evaluating local conditions, time constraints, financial limitations, and weather forecasts (Mahdi & Esztergár-Kiss, 2023). The digital era presents overwhelming options, including destinations, accommodations, and activities, leading to decision paralysis (Sarkar et al., 2022). The increasing number of service providers makes it challenging for tourists to choose the best travel plan (Chaudhari & Thakkar, 2019). Travel and tourism recommendation systems (TRS) have been developed to simplify the planning process, offer relevant suggestions, and enhance overall experiences by reducing effort. TRS uses data from user profiles, historical bookings, reviews, and social interactions to generate personalised recommendations (Hamid et al., 2021; Yuan & Zheng, 2024).

TRS has revolutionised the travel industry by providing personalised recommendations based on individual preferences, interests, and constraints. This approach not only helps discover new destinations but also boosts business growth and competitiveness for travel service providers (Banerjee et al., 2023; Wilcox, 2022). However, TRS faces challenges such as dynamic itinerary planning, mobile platforms, evaluation methods, group recommendation, social networks, integration, user modelling, privacy, and robustness. Traveller preferences significantly influence itinerary design. Current TRS requires travellers to predefine individual preferences for each point of interest (POI), which can be laborious in large-scale locations (Mahdi & Esztergár-Kiss, 2023). Additionally, TRS designs often rely on assumptions of certainty and consistency, which can lead to erroneous preference inputs. Decision-making in tourism involves balancing variables such as preferred destinations, activities, lodging, transportation, financial restrictions, personal interests, cultural background, and psychological factors. Travellers often alter travel plans due to unpredictable traffic, environmental conditions, and waiting hours. Thus, unpredictable tourist behaviour requires careful consideration of various locations, visit orders, and visitor demands. (Banerjee et al., 2023; Mahdi & Esztergár-Kiss, 2023; Wilcox, 2022).

The tourism industry is increasingly integrating social networks (SNs) with personalised recommendations based on social media data (Menk et al., 2019; Sarkar et al., 2022). Chaudhari and Thakkar (2019) also delve into tourism-related aspects such as hotel, restaurant, and attraction planning. Borràs et al. (2014) explore TRS technical aspects with a focus on traditional and tourism-specific technologies, artificial intelligence (AI), algorithms, datasets, and evaluation methods. Kontogianni and Alepis (2020) consider the broader context of smart tourism, emphasising privacy protection, user experience, and big data analytics. This diversity highlights the multifaceted nature of this research area and opens up various avenues for future exploration. There is a recognised need for further research into quality of service (QoS) improvement (Sarkar et al., 2022), unbiased data (Chaudhari and Thakkar, 2019), integration of social network-based recommender systems, and incorporation of user personality into recommendations (Menk et al., 2019). Additionally, there have also been calls for more research on diversity and trust in social recommendations (Borràs et al., 2014), as well as the need for large-scale user studies to evaluate the proposed systems (Kontogianni & Alepis, 2020).

The main research question is: How can TRS provide better recommendations for travellers by addressing the issues and challenges identified in the literature? This research aims to review contemporary TRS methodologies, assess strengths and weaknesses within existing frameworks and techniques, and propose recommendations to mitigate identified weaknesses and improve TRS performance. This can provide valuable insights and practical solutions for academic researchers and industry practitioners in TRS, as AI advancements have significantly impacted tourism, leading to smart tourism. Future research should evaluate proposed systems with real users to assess effectiveness, user acceptance, and practical challenges (Kontogianni & Alepis, 2020). The future of TRS depends on ethical concerns such as data privacy and user experience, as well as utilising advanced AI techniques like deep learning and natural language processing (NLP) to enhance accuracy and personalization. Collaboration across disciplines can help evolve TRS to provide a personalised, efficient, and enriching tourist experience.

# METHODOLOGY

## Data Collection and Pre-processing

Fundamental to building a robust TRS is an effective data collection and pre-processing technique (Belaidan et al., 2019) from sources such as user profiles, historical booking data, reviews, and social interactions (Cai et al., 2024; Olshannikova et al., 2017). Common data sources for developing TRS are TripAdvisor, Dataset\_tsmc, Hotel Reviews, dataset ubicomp, Flickr, and Agoda.com (Chouiref & Hayi, 2022). Data is collected, pre-processed, and transformed into a suitable format for analysis using techniques like data cleaning, normalization, and feature engineering to improve data quality and relevance (Arinez et al., 2020). Additionally, data anonymization and privacy protection measures may be implemented to safeguard sensitive user information.

## Feature Representation and Selection

Feature representation and selection are crucial for extracting meaningful patterns and insights from the pre-processed data that significantly influence the effectiveness and precision of TRS recommendations. This involves encoding relevant attributes of items like destinations, accommodations, and users into a structured format for recommendation (Pérez-Núñez et al., 2019). Feature selection techniques, such as dimensionality reduction and feature importance analysis, help identify informative features for modelling (Partridge & Calvo, 1998). Moreover, word embeddings (Bakarov, 2018) and image representations enable the integration of multimodal data sources, like textual descriptions and visual content, to enrich the representation of items and users in recommendations.

## Types of Tourism Recommendation Systems

TRS employs various methodologies, like collaborative filtering (Goldberg et al., 1992), which leverages the preferences and behaviours of similar users to make recommendations; content-based filtering (Pazzani & Billsus, 2007), where recommendations are based on the similarity of items to those previously viewed by the user; and hybrid filtering (Burke, 2002), which combines both content and collaborative approaches. Knowledge-based systems (Burke et al., 1996) consider constraints and user-item interactions, while demographic recommendation systems use demographic information and contextual data. The use of AI techniques like knowledge representation, optimization, clustering algorithms, multiagent systems, and natural language processing enhances recommendations and creates context-based, time-sensitive, and location-based social recommendation systems. These systems help identify industry strengths, weaknesses, opportunities, and threats.

Table 1 presents a SWOT analysis of TRS with a variety of strengths and trade-offs. Collaborative filtering (Goldberg et al., 1992) is effective based on collective user behaviour but faces challenges with new users. Content-based filtering (Pazzani & Billsus, 2007) is resourceful but limited, while context-aware filtering (Adomavičius & Tuzhilin, 2010) enhances personalisation but faces overspecialization and data scarcity. Hybrid approaches (Burke, 2002) promise higher accuracy but require complex management. Knowledge-based systems (Burke et al., 1996) offer depth but require ongoing maintenance. Social network-driven TRS (He & Chu, 2010) uses connections, while group-based systems (Masthoff, 2005) cater to collective preferences. The best TRS should be chosen based on specific use cases and priorities, with the potential for method fusion and targeted weakness mitigation to enhance the user experience.

<table>
<thead>
<tr class="header">
<th>Table 1. SWOT Analysis of TRS</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><table>
<thead>
<tr class="header">
<th><strong>TRS Type</strong></th>
<th><strong>Strengths</strong></th>
<th><strong>Weaknesses</strong></th>
<th><strong>Opportunities</strong></th>
<th><strong>Threats</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Collaborative Filtering Based TRS<sup>a</sup></td>
<td><p>Leverages user interactions and preferences for accurate recommendations</p>
<p>Can handle new items and users effectively</p></td>
<td><p>Cold start problem for new users and items</p>
<p>Vulnerable to shilling attacks</p></td>
<td><p>Personalisation based on user feedback and context</p>
<p>Integration with other recommendation techniques for enhanced accuracy</p></td>
<td><p>Privacy concerns regarding user data based on active user participation</p>
<p>Competing with newer recommendation methods</p></td>
</tr>
<tr class="even">
<td>Content Filtering Based TRS<sup>b</sup></td>
<td><p>Personalises recommendations based on item features and user preferences</p>
<p>Resilient to the cold start problem for new users</p></td>
<td><p>Limited to recommending items with available content metadata</p>
<p>Vulnerable to overspecialization</p></td>
<td><p>Integration with collaborative filtering for enhanced recommendations</p>
<p>Incorporation of hybrid approaches with collaborative filtering</p></td>
<td><p>Dependency on accurate and comprehensive item metadata</p>
<p>Risk of recommending redundant or irrelevant content</p></td>
</tr>
<tr class="odd">
<td>Context-Aware Filtering Based TRS<sup>c</sup></td>
<td>Increased relevance and personalization</td>
<td>Over-specialisation, cold start issues, data sparsity</td>
<td>Develop better algorithms and increase user trust</td>
<td>Privacy concerns, data consent challenges, potential bias, and competition from emerging technologies</td>
</tr>
<tr class="even">
<td>Hybrid TRS<sup>d</sup></td>
<td><p>Effective in mitigating the weaknesses of individual methods</p>
<p>Combines the strengths of multiple recommendation techniques for improved accuracy</p></td>
<td><p>Complexity in integrating different recommendation algorithms</p>
<p>Potential for increased computational overhead</p></td>
<td><p>Opportunities for innovation and exploration in combining several methods</p>
<p>Customisation options to suit specific application domains and user preferences</p></td>
<td><p>Challenges in maintaining system robustness and stability</p>
<p>Risk of user confusion or dissatisfaction due to complex recommendation logic</p></td>
</tr>
<tr class="odd">
<td>Knowledge Based TRS<sup>e</sup></td>
<td><p>Can handle cold start problems with explicit knowledge representation</p>
<p>Well-suited for domains with structured information and expertise</p></td>
<td><p>Dependency on accurate and up-to-date knowledge representation</p>
<p>Limited scalability in handling large knowledge bases</p></td>
<td><p>Opportunities for domain-specific customisation and fine-tuning</p>
<p>Potential for leveraging emerging AI techniques for knowledge representation and reasoning</p></td>
<td><p>Competition from more agile and data-driven recommendation systems</p>
<p>Challenges in acquiring and maintaining comprehensive domain knowledge</p></td>
</tr>
<tr class="even">
<td>Social Network TRS<sup>f</sup></td>
<td><p>Facilitates user engagement and interaction within social networks</p>
<p>Offers opportunities for serendipitous discovery and exploration</p></td>
<td><p>Privacy concerns regarding user data sharing and analysis</p>
<p>Challenges in accurately capturing user preferences and intentions from social interactions</p></td>
<td><p>Customisation for incorporating user privacy preferences</p>
<p>Potential for leveraging emerging social network analysis techniques for enhanced recommendation quality</p></td>
<td><p>Risks of user backlash or distrust due to perceived intrusiveness or manipulation</p>
<p>Dependence on social network platform stability and availability</p></td>
</tr>
<tr class="odd">
<td>Group Based TRS<sup>g</sup></td>
<td><p>Fostering collective decision-making and enjoyment</p>
<p>Offers opportunities for group discovery and shared experiences</p></td>
<td><p>Complexity in accommodating diverse group preferences</p>
<p>Dependency on accurate group profiling and interaction data</p></td>
<td><p>Customisation for different group dynamics and preferences</p>
<p>Potential for facilitating groups-based promotions and discounts</p></td>
<td><p>Challenges in balancing individual and group needs</p>
<p>Competition from traditional recommendation systems focused on individual users</p></td>
</tr>
</tbody>
</table></td>
</tr>
</tbody>
</table>

*Note.* TRS = Tourism Recommendation Systems. Adapted from <sup>a</sup>Adomavičius & Tuzhilin, 2005; Bao et al., 2015; Ren et al., 2017; Sarwar et al., 2001; Zhang et al., 2019. <sup>b</sup>Cibilić et al., 2021; Li et al., 2010. <sup>c</sup>Lathia et al., 2009; Liang et al., 2017; Zhang et al., 2018. <sup>d</sup>Aliannejadi & Crestani, 2018; Andrade & Almeida, 2013; Çano & Morisio, 2017; Fayyaz et al., 2020. <sup>e</sup>Gemmell et al., 2012. <sup>f</sup>Xiao et al., 2023. <sup>g</sup>Sarkar & Majumder, 2022.

# RESULTS AND DISCUSSION

## Evolution of Tourism Recommendation Systems

Initially, early TRS relied on simple rules or expert knowledge, often provided by travel agencies and guidebooks. However, these early RS had limitations in personalization and adaptability and were subject to bias (Li et al., 2010; Resnick & Varian, 1997). The emergence of online platforms marked a shift towards collaborative filtering algorithms, enabling access to vast amounts of travel-related information and user-generated reviews. Online travel platforms revolutionized travel planning by providing convenience, real-time pricing, and secure booking facilities (Filieri, 2015; Gretzel, 2011; Su et al., 2019; Xiang et al., 2017). Subsequently, personalization and context awareness became prominent (Öğüt & Onur Taş, 2012; Tarek et al., 2022; Xiang et al., 2017), with systems leveraging AI, machine learning (ML), and NLP to deliver personalized recommendations based on real-time contextual information and user data (Huang et al., 2023; Yochum et al., 2020; Zeng et al., 2023). The integration of mobile applications and wearable technology further enhanced the travel experience by offering personalized recommendations, interactive maps, AR experiences, and location-based services (Cibilić et al., 2021; Manggopa et al., 2022; Ojagh et al., 2020). Finally, there is a growing emphasis on incorporating social and environmental factors into recommendation systems, with sustainability, community-based tourism, and responsible travel recommendations based on user data and feedback gaining traction (Bargeman & Richards, 2020; Font et al., 2021; Hall et al.,2015; Mathew, 2022; Sharpley, 2020; Sun et al., 2020). Table 2 outlines the evolution of TRS, its key phases, and its ongoing efforts to enhance the travel experience with more personalized, relevant, and ethically conscious recommendations.

<table>
<thead>
<tr class="header">
<th>Table 2. Evolution of TRS</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><table>
<thead>
<tr class="header">
<th><strong>Phase</strong></th>
<th><strong>Description</strong></th>
<th><strong>Key Features/Technologies</strong></th>
<th><strong>References</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Early Recommendation Systems</td>
<td>Recommendations based on simple rules or expert knowledge</td>
<td>Travel agencies and guidebooks</td>
<td>(Li et al., 2010; Resnick &amp; Varian, 1997)</td>
</tr>
<tr class="even">
<td>Emergence of Online Platforms</td>
<td>Online platforms provide access to travel-related information and user-generated reviews</td>
<td>Collaborative filtering algorithms</td>
<td>(Filieri, 2015; Gretzel, 2011; Su et al., 2019; Xiang et al., 2017)</td>
</tr>
<tr class="odd">
<td>Personalization and Context Awareness</td>
<td>Shift towards personalization, using user preferences and contextual data to tailor recommendations</td>
<td><p>Sophisticated algorithms</p>
<p>Analysis of user data (past travel history, demographics)</p>
<p>Realtime contextual factors (location, time)</p></td>
<td>(Öğüt &amp; Onur Taş, 2012; Tarek et al., 2022; Xiang et al., 2017)</td>
</tr>
<tr class="even">
<td>AI and ML Integration, Realtime Personalization, NLP Integration, Context-aware Recommendations</td>
<td><p>Integration of AI and ML for real-time personalized recommendations</p>
<p>NLP enhances content processing</p>
<p>Context-aware systems adjust based on context</p></td>
<td><p>Collaborative filtering</p>
<p>Content-based filtering,</p>
<p>Hybrid models</p>
<p>Predictive analytics</p>
<p>NLP techniques</p>
<p>Contextual information</p>
<p>Dynamic adjustment</p></td>
<td>(Huang et al., 2023; Yochum et al., 2020; Zeng et al., 2023)</td>
</tr>
<tr class="odd">
<td>Mobile Applications and Wearable Technology</td>
<td>Mobile apps and wearable devices offer personalized recommendations, interactive maps, AR experiences, and location-based services</td>
<td>Mobile technology, AR</td>
<td>(Cibilić et al., 2021; Manggopa et al., 2022; Ojagh et al., 2020)</td>
</tr>
<tr class="even">
<td>Integration of Social and Environmental Factors</td>
<td>Emphasis on responsible and community-based travel</td>
<td>Sustainable tourism practices and eco-friendly accommodations</td>
<td>(Bargeman &amp; Richards, 2020; Font et al., 2021; Hall et al.,2015; Mathew, 2022; Sharpley, 2020; Sun et al., 2020)</td>
</tr>
</tbody>
</table></td>
</tr>
</tbody>
</table>

## Existing Tourism Recommendation Systems

Table 3 provides a summary and comparative analysis of each TRS that offers unique features and approaches to assist travellers in planning trips. PersonalTour (Lorenzi et al., 2011) and TravelBuddy (Fu et al., 2014; Jain et al., 2021) focus on personalized recommendations and real-time adjustments, while Photo2Trip (Gaggi, 2013; Wang et al., 2022; Yin et al., 2010) and TravelWithFriends (De Pessemier et al., 2015) use visual content and collaborative filtering. TripAdvisor (Amaral et al., 2014; Belaidan et al., 2019; Filieri et al., 2020; Valdivia et al., 2019) and TripHobo offer user-generated content and comprehensive trip planning, but have limitations such as authenticity concerns and a lack of detailed time management (Belaidan et al., 2019). Overall, the choice of recommendation system depends on individual preferences and the specific needs of travellers.

<table>
<thead>
<tr class="header">
<th>Table 3. Existing TRS</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><table>
<thead>
<tr class="header">
<th><strong>TRS</strong></th>
<th><strong>Key Features</strong></th>
<th><strong>Advantages</strong></th>
<th><strong>Disadvantages</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>PersonalTour<sup>a</sup></td>
<td>Uses distributed AI and multiagent systems, autonomous agents, and flexible recommendations</td>
<td>Outperformed manual travel agents with 76.59% accuracy compared to human experts</td>
<td>May not fully replace human expertise</td>
</tr>
<tr class="even">
<td>TravelBuddy<sup>b</sup></td>
<td>Real-time route planning algorithm, knowledge graph attention network, interactive interface</td>
<td>Continuously customises routes based on user feedback and offers personalised advice</td>
<td>Requires active user input and interaction</td>
</tr>
<tr class="odd">
<td>Photo2Trip<sup>c</sup></td>
<td>Uses geotagged photos, mean-shift clustering, and collaborative filtering</td>
<td>Recommends popular routes, considers visit time, location, and duration</td>
<td>Relies heavily on visual content</td>
</tr>
<tr class="even">
<td>TravelWithFriends<sup>d</sup></td>
<td>Hybrid system using content-based, collaborative filtering, and knowledge-based methods</td>
<td>Provides personalised recommendations based on user constraints and filters destinations by ratings</td>
<td>May not offer detailed explanations for suggestions</td>
</tr>
<tr class="odd">
<td>TripAdvisor<sup>e</sup></td>
<td>User-generated content platform, five-point rating scale, content-based method</td>
<td>Offers word-of-mouth reviews, a platform for searches, and bookings</td>
<td>Concerns about cluttered displays and fake reviews</td>
</tr>
<tr class="even">
<td>TripHobo<sup>f</sup></td>
<td>Offers a variety of locations, restaurants, package tours, and a drag-and-drop interface</td>
<td>Covers attractions, itinerary planning, accommodations, and local transportation</td>
<td>May lack consideration of time spent at each location</td>
</tr>
</tbody>
</table></td>
</tr>
</tbody>
</table>

*Note.* TRS = Tourism Recommendation Systems. Adapted from <sup>a</sup>Lorenzi et al., 2011. <sup>b</sup>Fu et al., 2014; Jain et al., 2021. <sup>c</sup>Gaggi, 2013; Wang et al., 2022; Yin et al., 2010. <sup>d</sup>De Pessemier et al., 2015. <sup>e</sup>Amaral et al., 2014; Belaidan et al., 2019; Filieri et al., 2020; Valdivia et al., 2019. <sup>f</sup>Belaidan et al., 2019.

## Recommendation Algorithms and Techniques

A wide range of algorithms and techniques have been developed to tackle the recommendation problem, each with its strengths and limitations. Some common approaches include:

1)  > Matrix factorization: Matrix factorization techniques decompose the user-item interaction matrix into lower dimensional latent factors, capturing the underlying preferences and patterns in the (Sarkar et al., 2022; Takács & Tikk, 2012).data. Collaborative filtering-based recommendations commonly employ Singular Value Decomposition (SVD) and Alternating Least Squares (ALS) (Chaudhari & Thakkar, 2019; Tarek et al., 2022).

2)  > Deep Learning: techniques, such as neural networks and deep autoencoders, have gained popularity for their ability to model complex interactions and nonlinear relationships in the data. Deep learning models can learn hierarchical representations of users and items, enabling more accurate and expressive recommendations (Noshad et al., 2021).

3)  > Reinforcement Learning: Reinforcement learning techniques, such as Multiarmed Bandits and Deep QNetworks, enable sequential decision-making Deep learning in recommendation systems. These methods learn to optimize recommendation policies through trial-and-error interactions with users, maximizing long-term rewards and user satisfaction (Filieri et al., 2020; Kontogianni & Alepis, 2020; Sarkar et al., 2022).
    
    By leveraging these components and techniques, TRS can deliver personalized and relevant recommendations to enhance user satisfaction, engagement, loyalty, and satisfaction in travel planning.
    
    1.  ## Future Directions and Research Opportunities

The field of travel recommendation systems is ripe for innovation and advancement due to technological advancements and changing user preferences. Researchers and practitioners can explore new directions and address unresolved challenges. This section identifies open research questions and areas that require further investigation in the future. By exploring these areas, researchers and practitioners can create more effective, engaging, and trustworthy recommendations for users.

## *Context-Aware Recommendation*

One promising avenue for future research is the development of context-aware recommendation systems that can dynamically adapt recommendations based on the user's context and situational factors. By incorporating contextual information such as time, location, weather, and user activity, TRS can deliver more relevant and timely recommendations that meet the user's immediate needs and preferences. Future research in this area could focus on developing novel algorithms and techniques for context-aware recommendation, as well as exploring the impact of contextual factors on user engagement and satisfaction.

## *Personalization at Scale*

With the proliferation of data and the increasing complexity of user preferences, there is a growing need for scaled personalization in TRS. Future research could focus on developing scalable algorithms and techniques that can handle large volumes of data and deliver personalized recommendations to a diverse range of users. Additionally, research could explore methods for incorporating implicit and explicit user feedback into the recommendation process to enhance personalization and recommendation accuracy.

## *Explainable AI and Transparency*

As recommendation systems become increasingly sophisticated, there is a growing need for transparency and explainability to build trust and confidence among users. Future research could focus on developing explainable AI techniques that provide users with insights into how recommendations are generated and why specific recommendations are made. By making recommendation systems more transparent and interpretable, users can better understand and trust the recommendations they receive, leading to increased user satisfaction and engagement.

## *Multimodal Recommendation*

With the rise of multimedia content on travel platforms, there is an opportunity to explore multimodal recommendation systems that can leverage diverse data sources, such as text, images, and videos. Future research could focus on developing algorithms and techniques for integrating and analysing multimodal data to generate more comprehensive and engaging recommendations. Additionally, research could explore methods for incorporating user-generated content, such as reviews and social media posts, into the recommendation process to enrich the recommendation experience.

## *Ethical and Fair Recommendation*

As recommendation systems increasingly shape user experiences and decision making, there is a growing need to address ethical and fairness considerations in the design and implementation of these systems. Future research could focus on developing ethical guidelines and frameworks for designing recommendation systems that prioritize user privacy, autonomy, and wellbeing. Additionally, research could explore methods for mitigating algorithmic bias and discrimination in recommendation systems to ensure fair and equitable treatment of all users.

# CONCLUSION

This review highlights key findings and implications for the future of travel recommendation systems, including context-aware recommendation, personalization at scale, explainable AI, multimodal recommendation, and ethical and fair recommendation while mitigating algorithmic bias and discrimination. Researchers should explore new directions, prioritize user-centric design principles, transparency, and user engagement in the development and implementation of TRS. Collaboration between academia and industry can foster innovation and accelerate the development of next-generation systems that meet the evolving needs and preferences of travellers. In conclusion, travel recommendation systems are crucial in shaping how travellers discover, plan, and experience their journeys. By continuing to innovate and address key challenges, researchers and practitioners can create more effective, engaging, and trustworthy recommendation experiences that enhance the travel planning process for users around the world.

# ACKNOWLEDGEMENTS/FUNDING

Only include Acknowledgements text in the final submission paper.

# CONFLICT OF INTEREST STATEMENT

Only include CONFLICT OF INTERESTS text in the final submission paper.

# AUTHORS’ CONTRIBUTIONS

# 

# REFERENCES

Adomavičius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. *IEEE Transactions on Knowledge and Data Engineering, 17*(6), 734–749. [<span class="underline">https://doi.org/10.1109/tkde.2005.99</span>](https://doi.org/10.1109/tkde.2005.99)

Adomavičius, G., & Tuzhilin, A. (2010). Context-Aware recommender systems. In *Springer eBooks* (pp. 217–253). [<span class="underline">https://doi.org/10.1007/978-0-387-85820-3\_7</span>](https://doi.org/10.1007/978-0-387-85820-3_7)

Aliannejadi, M., & Crestani, F. (2018). Personalized Context-Aware Point of Interest recommendation. *ACM Transactions on Office Information Systems*, *36*(4), 1–28. [<span class="underline">https://doi.org/10.1145/3231933</span>](https://doi.org/10.1145/3231933)

Amaral, F., Tiago, T., & Tiago, F. (2014). User-generated content: Tourists’ profiles on TripAdvisor. *International Journal of Strategic Innovative Marketing*, *1*(3), 137-145. [<span class="underline">https://doi.org/10.15556/ijsim.01.03.002</span>](https://doi.org/10.15556/ijsim.01.03.002)

Andrade, M. T., & Almeida, F. (2013). Novel hybrid approach to content recommendation based on predicted profiles. In *2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing* (pp. 507–514). IEEE. [<span class="underline">https://doi.org/10.1109/uic-atc.2013.49</span>](https://doi.org/10.1109/uic-atc.2013.49)

Arinez, J., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial intelligence in Advanced Manufacturing: current status and future outlook. *Journal of Manufacturing Science and Engineering*, *142*(11). [<span class="underline">https://doi.org/10.1115/1.4047855</span>](https://doi.org/10.1115/1.4047855)

Bakarov, A. (2018). A survey of word embeddings evaluation methods. *arXiv preprint arXiv:1801.09536*. <span class="underline">https://doi.org/10.48550/arXiv.1801.09536</span>

Banerjee, A., Banik, P., & Wörndl, W. (2023). A review on individual and multistakeholder fairness in tourism recommender systems. *Frontiers in Big Data*, *6*, 1168692. [<span class="underline">https://doi.org/10.3389/fdata.2023.1168692</span>](https://doi.org/10.3389/fdata.2023.1168692)

Bao, J., Zheng, Y., Wilkie, D. A., & Mokbel, M. F. (2015). Recommendations in location-based social networks: a survey. *GeoInformatica/Geoinformatica*, *19*(3), 525–565. [<span class="underline">https://doi.org/10.1007/s10707-014-0220-8</span>](https://doi.org/10.1007/s10707-014-0220-8)

Bargeman, B., & Richards, G. (2020). A new approach to understanding tourism practices. *Annals of Tourism Research*, *84*, 102988. [<span class="underline">https://doi.org/10.1016/j.annals.2020.102988</span>](https://doi.org/10.1016/j.annals.2020.102988)

Belaidan, S. L. M., Yee, L. Y., Rahman, N. a. A., & Harun, K. S. (2019). Implementing k-means clustering algorithm in collaborative trip advisory and planning system. *Periodicals of Engineering and Natural Sciences*, *7*(2), 723-740. [<span class="underline">https://doi.org/10.1016/j.annals.2020.102988</span>](https://doi.org/10.1016/j.annals.2020.102988)

Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. *Expert Systems With Applications*, *41*(16), 7370–7389. [<span class="underline">https://doi.org/10.1016/j.eswa.2014.06.007</span>](https://doi.org/10.1016/j.eswa.2014.06.007)

Burke, R. (2002). Hybrid recommender systems: Survey and experiments. *User modeling and user-adapted interaction*, *12*(4), 331-370. [<span class="underline">https://doi.org/10.1023/A:1021240730564</span>](https://doi.org/10.1023/A:1021240730564)

Burke, R., Hammond, K. J., & Young, B. C. (1996). Knowledge-based navigation of complex information spaces. *National Conference on Artificial Intelligence*, 462–468. [<span class="underline">https://www.aaai.org/Papers/AAAI/1996/AAAI96-069.pdf</span>](https://www.aaai.org/Papers/AAAI/1996/AAAI96-069.pdf)

Cai, Y., Li, G., Wen, L., & Chang, L. (2024). Intellectual landscape and emerging trends of big data research in hospitality and tourism: A scientometric analysis. *International Journal of Hospitality Management*, *117*, 103633. [<span class="underline">https://doi.org/10.1016/j.ijhm.2023.103633</span>](https://doi.org/10.1016/j.ijhm.2023.103633)

Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. *Intelligent Data Analysis*, *21*(6), 1487–1524. [<span class="underline">https://doi.org/10.3233/ida-163209</span>](https://doi.org/10.3233/ida-163209)

Chaudhari, K., & Thakkar, A. (2019). A comprehensive survey on travel recommender systems. *Archives of Computational Methods in Engineering*, *27*(5), 1545–1571. [<span class="underline">https://doi.org/10.1007/s11831-019-09363-7</span>](https://doi.org/10.1007/s11831-019-09363-7)

Chouiref, Z., & Hayi, M. Y. (2022). Toward preference and context-aware hybrid tourist recommender system based on machine learning techniques. *Revue d’Intelligence Artificielle*, *36*(2), 195–208. [<span class="underline">https://doi.org/10.18280/ria.360203</span>](https://doi.org/10.18280/ria.360203)

Cibilić, I., Poslončec-Petrić, V., & Tominić, K. (2021). Implementing augmented reality in tourism. *Proceedings of the ICA*, *4*, 1–5. [<span class="underline">https://doi.org/10.5194/ica-proc-4-21-2021</span>](https://doi.org/10.5194/ica-proc-4-21-2021)

De Pessemier, T., Dhondt, J., Vanhecke, K., & Martens, L. (2015). Travelwithfriends: a hybrid group recommender system for travel destinations. In *Workshop on tourism recommender systems (touRS15), in conjunction with the 9th ACM conference on recommender systems (recsys 2015)* (pp. 51-60).

Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: algorithms, challenges, metrics, and business opportunities. *Applied Sciences*, *10*(21), 7748. [<span class="underline">https://doi.org/10.3390/app10217748</span>](https://doi.org/10.3390/app10217748)

Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. *Journal of Business Research*, *68*(6), 1261–1270. [<span class="underline">https://doi.org/10.1016/j.jbusres.2014.11.006</span>](https://doi.org/10.1016/j.jbusres.2014.11.006)

Filieri, R., Açikgöz, F., Ndou, V., & Dwivedi, Y. K. (2020). Is TripAdvisor still relevant? The influence of review credibility, review usefulness, and ease of use on consumers’ continuance intention. *International Journal of Contemporary Hospitality Management*, *33*(1), 199–223. [<span class="underline">https://doi.org/10.1108/ijchm-05-2020-0402</span>](https://doi.org/10.1108/ijchm-05-2020-0402)

Font, X., Torres-Delgado, A., Crabolu, G., Palomo Martinez, J., Kantenbacher, J., & Miller, G. (2021). The impact of sustainable tourism indicators on Destination Competitiveness: The European Tourism Indicator System. *Journal of Sustainable Tourism*, *31*(7), 1608–1630. [<span class="underline">https://doi.org/10.1080/09669582.2021.1910281</span>](https://doi.org/10.1080/09669582.2021.1910281)

Fu, C. Y., Hu, M. C., Lai, J. H., Wang, H., & Wu, J. L. (2014). Travelbuddy: interactive travel route recommendation with a visual scene interface. In *MultiMedia Modeling: 20th Anniversary International Conference, MMM 2014, Dublin, Ireland, January 6-10, 2014, Proceedings, Part I 20* (pp. 219-230). Springer International Publishing. [<span class="underline">https://doi.org/10.1007/978-3-319-04114-8\_19</span>](https://doi.org/10.1007/978-3-319-04114-8_19)

Gaggi, O. (2013). Discovering local attractions from geo-tagged photos. In *Proceedings of the 28th Annual ACM Symposium on Applied Computing* (pp. 730-735). [<span class="underline">https://doi.org/10.1145/2480362.2480502</span>](https://doi.org/10.1145/2480362.2480502)

Gemmell, J., Schimoler, T., Mobasher, B., & Burke, R. (2012). Resource recommendation in social annotation systems: A linear-weighted hybrid approach. *Journal of Computer and System Sciences*, *78*(4), 1160–1174. [<span class="underline">https://doi.org/10.1016/j.jcss.2011.10.006</span>](https://doi.org/10.1016/j.jcss.2011.10.006)

Goldberg, D. T., Nichols, D. M., Oki, B. M., & Terry, D. B. (1992). Using collaborative filtering to weave an information tapestry. *Communications of the ACM*, *35*(12), 61–70. [<span class="underline">https://doi.org/10.1145/138859.138867</span>](https://doi.org/10.1145/138859.138867)

Gretzel, U. (2011). Intelligent systems in tourism: A social science perspective. *Annals of tourism research*, *38*(3), 757-779. [<span class="underline">https://doi.org/10.1016/j.annals.2011.04.014</span>](https://doi.org/10.1016/j.annals.2011.04.014)

Hall, C. M., Gößling, S., & Scott, D. (2015). The evolution of sustainable development and sustainable tourism. In *The Routledge handbook of tourism and sustainability* (pp. 16–35). Routledge. [<span class="underline">https://doi.org/10.4324/9780203072332.ch2</span>](https://doi.org/10.4324/9780203072332.ch2)

Hamid, R. A., Albahri, A. S., Alwan, J. K., Al-Qaysi, Z., Albahri, O. S., Zaidan, A. A., Alnoor, A., Alamoodi, A. H., & Zaidan, B. B. (2021). How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management. *Computer Science Review*, *39*, 100337. [<span class="underline">https://doi.org/10.1016/j.cosrev.2020.100337</span>](https://doi.org/10.1016/j.cosrev.2020.100337)

He, J., & Chu, W. W. (2010). A social network-based Recommender System (SNRS). *Data Mining for Social Network Data*, 47–74. [<span class="underline">https://doi.org/10.1007/978-1-4419-6287-4\_4</span>](https://doi.org/10.1007/978-1-4419-6287-4_4)

Huang, S., Wu, X., Wu, X., & Wang, K. (2023). Sentiment analysis algorithm using contrastive learning and adversarial training for poi recommendation. *Social Network Analysis and Mining*, *13*(1), 75. [<span class="underline">https://doi.org/10.1007/s13278-023-01076-x</span>](https://doi.org/10.1007/s13278-023-01076-x)

Jain, D., Mistry, G., Inchanalkar, T., Dholakia, R., Dalal, J., & Rote, U. (2021). Travel buddy: Travel companion using knowledge graph attention networks. *SSRN Electronic Journal*. [<span class="underline">https://doi.org/10.2139/ssrn.3867158</span>](https://doi.org/10.2139/ssrn.3867158)

Kontogianni, A., & Alepis, E. (2020). Smart tourism: State of the art and literature review for the last six years. *Array*, *6*, 100020. [<span class="underline">https://doi.org/10.1016/j.array.2020.100020</span>](https://doi.org/10.1016/j.array.2020.100020)

Lathia, N., Hailes, S., & Capra, L. (2009). Temporal collaborative filtering with adaptive neighbourhoods. In *Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval* (pp. 955–960). [<span class="underline">https://doi.org/10.1145/1571941.1572133</span>](https://doi.org/10.1145/1571941.1572133)

Liang, T., He, L., Lu, C.-T., Chen, L., Yu, P. S., & Wu, J. (2017). A broad learning approach for context-aware mobile application recommendation. *2017 IEEE International Conference on Data Mining (ICDM)*. [<span class="underline">https://doi.org/10.1109/icdm.2017.121</span>](https://doi.org/10.1109/icdm.2017.121)

Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. In *Proceedings of the 19th international conference on World wide web* (pp. 661-670). [<span class="underline">https://doi.org/10.1145/1772690.1772758</span>](https://doi.org/10.1145/1772690.1772758)

Lorenzi, F., Loh, S., & Abel, M. (2011). PersonalTour: A recommender system for travel packages. In *2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology* (Vol. 2, pp. 333-336). IEEE. [<span class="underline">https://doi.org/10.1109/WI-IAT.2011.69</span>](https://doi.org/10.1109/WI-IAT.2011.69)

Mahdi, A. J., & Esztergár-Kiss, D. (2023). Supporting scheduling decisions by using genetic algorithm based on tourists’ preferences. *Applied Soft Computing*, *148*, 110857. [<span class="underline">https://doi.org/10.1016/j.asoc.2023.110857</span>](https://doi.org/10.1016/j.asoc.2023.110857)

Menk, A., Sebastia, L., & Ferreira, R. (2019). Recommendation systems for tourism based on social networks: A survey. *arXiv preprint arXiv:1903.12099*. [<span class="underline">https://doi.org/10.48550/arXiv.1903.12099</span>](https://doi.org/10.48550/arXiv.1903.12099)

Manggopa, H. K., Komansilan, T., Kumajas, S., & Batmetan, J. R. (2022). Augmented Reality for Geospatial in E-Tourism: current status, research trends, and future directions. *International Journal of Information Technology and Education*, *1*(3), 38–55. [<span class="underline">https://doi.org/10.62711/ijite.v1i3.58</span>](https://doi.org/10.62711/ijite.v1i3.58)

Masthoff, J. (2005). The pursuit of satisfaction: affective state in group recommender systems. In *International Conference on User Modeling* (pp. 297-306). Berlin, Heidelberg: Springer Berlin Heidelberg. [<span class="underline">https://doi.org/10.1007/11527886\_39</span>](https://doi.org/10.1007/11527886_39)

Mathew, P. V. (2022). Sustainable tourism development: discerning the impact of responsible tourism on community well-being. *Journal of Hospitality and Tourism Insights*, *5*(5), 987-1001. [<span class="underline">https://doi.org/10.1108/JHTI-02-2021-0052</span>](https://doi.org/10.1108/JHTI-02-2021-0052)

Noshad, Z., Bouyer, A., & Noshad, M. (2021). Mutual information-based recommender system using autoencoder. *Applied Soft Computing*, *109*, 107547. <span class="underline">https://doi.org/10.1016/j.asoc.2021.107547</span>

Öğüt, H., & Onur Taş, B. K. (2012). The influence of internet customer reviews on the online sales and prices in hotel industry. *The Service Industries Journal*, *32*(2), 197–214. [<span class="underline">https://doi.org/10.1080/02642069.2010.529436</span>](https://doi.org/10.1080/02642069.2010.529436)

Ojagh, S., Malek, M. R., Saeedi, S., & Liang, S. (2020). A location-based orientation-aware recommender system using IOT smart devices and social networks. *Future Generation Computer Systems*, *108*, 97–118. [<span class="underline">https://doi.org/10.1016/j.future.2020.02.041</span>](https://doi.org/10.1016/j.future.2020.02.041)

Olshannikova, E., Olsson, T., Huhtamäki, J., & Kärkkäinen, H. (2017). Conceptualizing big social data. *Journal of Big Data*, *4*(1), 1-19. [<span class="underline">https://doi.org/10.1186/s40537-017-0063-x</span>](https://doi.org/10.1186/s40537-017-0063-x)

Partridge, M., & Calvo, R. A. (1998). Fast dimensionality reduction and simple PCA. *Intelligent Data Analysis*, *2*(3), 203–214. [<span class="underline">https://doi.org/10.1016/s1088-467x(98)00024-9</span>](https://doi.org/10.1016/s1088-467x\(98\)00024-9)

Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In *The adaptive web: methods and strategies of web personalization* (pp. 325-341). Berlin, Heidelberg: Springer Berlin Heidelberg. [<span class="underline">https://doi.org/10.1007/978-3-540-72079-9\_10</span>](https://doi.org/10.1007/978-3-540-72079-9_10)

Pérez-Núñez, P., Luaces, O., Bahamonde, A., & Díez, J. (2019). Improving recommender systems by encoding items and user profiles considering the order in their consumption history. *Progress in Artificial Intelligence (Zeitschrift. Print)*, *9*(1), 67–75. [<span class="underline">https://doi.org/10.1007/s13748-019-00199-7</span>](https://doi.org/10.1007/s13748-019-00199-7)

Ren, X., Song, M., Haihong, E., & Song, J. (2017). Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. *Neurocomputing*, *241*, 38–55. [<span class="underline">https://doi.org/10.1016/j.neucom.2017.02.005</span>](https://doi.org/10.1016/j.neucom.2017.02.005)

Resnick, P., & Varian, H. R. (1997). Recommender systems. *Communications of the ACM*, *40*(3), 56-58. [<span class="underline">https://doi.org/10.1145/245108.245121</span>](https://doi.org/10.1145/245108.245121)

Sarkar, J. L., & Majumder, A. (2022). gTour: Multiple itinerary recommendation engine for group of tourists. *Expert Systems With Applications*, *191*, 116190. [<span class="underline">https://doi.org/10.1016/j.eswa.2021.116190</span>](https://doi.org/10.1016/j.eswa.2021.116190)

Sarkar, J. L., Majumder, A., Panigrahi, C. R., Roy, S., & Pati, B. (2022). Tourism recommendation system: a survey and future research directions. *Multimedia Tools and Applications*, *82*(6), 8983–9027. [<span class="underline">https://doi.org/10.1007/s11042-022-12167-w</span>](https://doi.org/10.1007/s11042-022-12167-w)

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In *Proceedings of the 10th international conference on World Wide Web* (pp. 285-295). [<span class="underline">https://doi.org/10.1145/371920.372071</span>](https://doi.org/10.1145/371920.372071)

Sharpley, R. (2020). Tourism, Sustainable Development and the theoretical divide: 20 Years on. *Journal of Sustainable Tourism*, *28*(11), 1932–1946. [<span class="underline">https://doi.org/10.1080/09669582.2020.1779732</span>](https://doi.org/10.1080/09669582.2020.1779732)

Su, X., Sperli, G., Moscato, V., Picariello, A., Esposito, C., & Choi, C. (2019). An edge intelligence empowered Recommender System Enabling Cultural Heritage Applications. *IEEE Transactions on Industrial Informatics*, *15*(7), 4266–4275. [<span class="underline">https://doi.org/10.1109/tii.2019.2908056</span>](https://doi.org/10.1109/tii.2019.2908056)

Sun, Y.-Y., Lin, P.-C., & Higham, J. (2020). Managing tourism emissions through optimizing the Tourism Demand Mix: Concept and analysis. *Tourism Management*, *81*, 104161. [<span class="underline">https://doi.org/10.1016/j.tourman.2020.104161</span>](https://doi.org/10.1016/j.tourman.2020.104161)

Takács, G., & Tikk, D. (2012). Alternating least squares for personalized ranking. In *Proceedings of the sixth ACM conference on Recommender systems* (pp. 83-90). [<span class="underline">https://doi.org/10.1145/2365952.2365972</span>](https://doi.org/10.1145/2365952.2365972)

Tarek, I. H., Munna, F. U., Mojumder, A. T., Rahman, M. M., Hossain, M. S., & Andersson, K. (2022). A Hybrid Hotel recommendation using collaborative, content based and knowledge based approach. *Intelligent Computing & Optimization*, 1049–1057. [<span class="underline">https://doi.org/10.1007/978-3-031-19958-5\_98</span>](https://doi.org/10.1007/978-3-031-19958-5_98)

Valdivia, A., Hrabova, E., Chaturvedi, I., Luzón, M. V., Troiano, L., Wang, Z., & Herrera, F. (2019). Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods. *Neurocomputing*, *353*, 3–16. [<span class="underline">https://doi.org/10.1016/j.neucom.2018.09.096</span>](https://doi.org/10.1016/j.neucom.2018.09.096)

Wang, J., Wu, N., & Zhao, X. (2022). Personalized route recommendation with neural network enhanced search algorithm. *IEEE Transactions on Knowledge and Data Engineering (Print)*, *34*(12), 5910–5924. [<span class="underline">https://doi.org/10.1109/tkde.2021.3068479</span>](https://doi.org/10.1109/tkde.2021.3068479)

Wilcox, J. (2022). How can we get more accurate: Recommendations about human judgment. *SpringerBriefs in Psychology*, 113–133. [<span class="underline">https://doi.org/10.1007/978-3-031-19205-0\_8</span>](https://doi.org/10.1007/978-3-031-19205-0_8)

Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. *Tourism Management*, *58*, 51–65. [<span class="underline">https://doi.org/10.1016/j.tourman.2016.10.001</span>](https://doi.org/10.1016/j.tourman.2016.10.001)

Xiao, L., Sun, L., Ling, M., & Peng, Y. (2023). A survey of graph neural network based recommendation in social networks. *Neurocomputing*, *549*, 126441. [<span class="underline">https://doi.org/10.1016/j.neucom.2023.126441</span>](https://doi.org/10.1016/j.neucom.2023.126441)

Yin, H., Lu, X., Wang, C., Yu, N., & Zhang, L. (2010). Photo2trip: an interactive trip planning system based on geo-tagged photos. In *Proceedings of the 18th ACM international conference on Multimedia* (pp. 1579-1582). [<span class="underline">https://doi.org/10.1145/1873951.1874291</span>](https://doi.org/10.1145/1873951.1874291)

Yochum, P., Chang, L., Gu, T., & Zhu, M. (2020). Linked open data in location-based recommendation system on Tourism Domain: A survey. *IEEE Access*, *8*, 16409–16439. [<span class="underline">https://doi.org/10.1109/access.2020.2967120</span>](https://doi.org/10.1109/access.2020.2967120)

Yuan, Y., & Zheng, W. (2024). Your trip, your way: An adaptive tourism recommendation system. *Applied Soft Computing*, *154*, 111330. [<span class="underline">https://doi.org/10.1016/j.asoc.2024.111330</span>](https://doi.org/10.1016/j.asoc.2024.111330)

Zeng, L., Guan, J., & Chen, B. (2023). MSBPR: A multi-pairwise preference and similarity based Bayesian personalized ranking method for recommendation. *Knowledge-Based Systems*, *260*, 110165. [<span class="underline">https://doi.org/10.1016/j.knosys.2022.110165</span>](https://doi.org/10.1016/j.knosys.2022.110165)

Zhang, S., Liu, W., Deng, X., Xu, Z., & Choo, K. R. (2018). Micro-blog topic recommendation based on knowledge flow and user selection. *Journal of Computational Science*, *26*, 512–521. [<span class="underline">https://doi.org/10.1016/j.jocs.2017.10.021</span>](https://doi.org/10.1016/j.jocs.2017.10.021)

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. *ACM computing surveys (CSUR)*, *52*(1), 1-38. [<span class="underline">https://doi.org/10.1145/3285029</span>](https://doi.org/10.1145/3285029)

<table>
<tbody>
<tr class="odd">
<td><blockquote>
<p><img src="66587a89aeb87_media/media/image2.png" style="width:1.01042in;height:0.36111in" alt="A picture containing text, clipart Description automatically generated" /></p>
</blockquote></td>
<td>© 2024 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).</td>
</tr>
</tbody>
</table>

1.  <sup>\*</sup> Corresponding author. *E-mail address*: <donottypehere@email.com> (Add the e-mail in the final camera-ready submission)