A Review on Content Based Recommender Systems in Tourism

Bentaleb, Asmae; Idrissi, Younès El Bouzekri El; Lahcen, Ayoub Ait

doi:10.1007/978-3-030-73882-2_48

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Below we mentioned some of the important works in nongraph-based travel recommendation systems. Analysis of tourism recommendation systems works since 2008, focusing on content-based suggestions, has been mentioned in the conference survey article proposed by Bentaleb et al (2021) [2] . The author discusses several methods for making traditional recommendations, such as content-based methods, collaborative filtering, and hybrid methods.…”

Section: A Non-graph-based Travel Recommendation Systemsmentioning

confidence: 99%

Travel recommendation system using graph neural networks

Prakki,

Daneshyari

2023

Int. J. Comput. Artif. Intell.

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There are many applications and uses of recommendation systems. For any recommendation system, the user-to-item interactions are important which can also be seen as graphs. We are focusing on travel or place recommendations. There are very few works on travel or trip recommendation systems using Graph Neural Networks (GNN) leveraging user-to-item interactions. In this work, we have implemented travel or place recommendations using the LightGCN model and compared it with the implementation of the traditional non-graph-based collaborative filtering Matrix Factorization (MF) approach. We have then shown that the LightGCN model performs better for travel or place recommendations than the Matrix Factorization approach. We achieved very good results [203% increase in precision, a 167% increase in recall, and a 98.6% NDCG increase in metrics] using a graph based LightGCN model for travel or place recommendations compared to the Matrix Factorization approach.

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Section: A Non-graph-based Travel Recommendation Systemsmentioning

confidence: 99%

Travel recommendation system using graph neural networks

Prakki,

Daneshyari

2023

Int. J. Comput. Artif. Intell.

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“…However, new tourist destinations cannot be applied because no tourist information is available [ 13 ]. To solve the cold start problem, a CB-based RS that recommends tourist destinations according to their relevance can also recommend similar tourist destinations using tourist destination information [ 14 ]. CB can provide recommendations without interaction data between tourists and tourist destinations.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Context-Aware Recommendation System for Tourism

Yoon

Choi

2023

Sensors

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Recently, the tourism trend has been shifting towards the Tourism 2.0 paradigm due to increased travel experiences and the increase in acquiring and sharing information through the Internet. The Tourism 2.0 paradigm requires developing intelligent tourism service tools for positive effects such as time savings and marketing utilization. Existing tourism service tools recommend tourist destinations based on the relationship between tourists and tourist destinations or tourism patterns, so it is difficult to make recommendations in situations where information is insufficient or changes in real time. In this paper, we propose a real-time recommendation system for tourism (R2Tour) that responds to changing situations in real time, such as external factors and distance information, and recommends customized tourist destinations according to the type of tourist. R2Tour trains a machine learning model with situational information such as temperature and precipitation and tourist profiles such as gender and age to recommend the top five nearby tourist destinations. To verify the recommendation performance of R2Tour, six machine learning models, including K-NN and SVM, and information on tourist attractions in Jeju Island were used. As a result of the experiment, R2Tour was verified with accuracy of 77.3%, micro-F1 0.773, and macro-F1 0.415. Since R2Tour trains tourism patterns based on situational information, it is possible to recommend new tourist destinations and respond to changing situations in real time. In the future, R2Tour can be installed in vehicles to recommend nearby tourist destinations or expanded to tasks in the tourism industry, such as a smart target advertising system.

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