BTRec: BERT-Based Trajectory Recommendation for Personalized Tours

• URL: http://arxiv.org/abs/2310.19886v1
• Date: Mon, 30 Oct 2023 18:00:26 GMT
• Title: BTRec: BERT-Based Trajectory Recommendation for Personalized Tours
• Authors: Ngai Lam Ho, Roy Ka-Wei Lee, Kwan Hui Lim
• Abstract summary: Many tour recommendation tools only take into account a limited number of factors, such as popular Points of Interest (POIs) and routing constraints. We propose an iterative algorithm in this paper, namely: BTREC (BERT-based Trajectory Recommendation), that extends from the POIBERT embedding algorithm to recommend personalized itineraries on POIs. Our recommendation system can create a travel itinerary that maximizes POIs visited, while also taking into account user preferences for categories of POIs and time availability.
• Score: 6.753123338256321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An essential task for tourists having a pleasant holiday is to have a well-planned itinerary with relevant recommendations, especially when visiting unfamiliar cities. Many tour recommendation tools only take into account a limited number of factors, such as popular Points of Interest (POIs) and routing constraints. Consequently, the solutions they provide may not always align with the individual users of the system. We propose an iterative algorithm in this paper, namely: BTREC (BERT-based Trajectory Recommendation), that extends from the POIBERT embedding algorithm to recommend personalized itineraries on POIs using the BERT framework. Our BTREC algorithm incorporates users' demographic information alongside past POI visits into a modified BERT language model to recommend a personalized POI itinerary prediction given a pair of source and destination POIs. Our recommendation system can create a travel itinerary that maximizes POIs visited, while also taking into account user preferences for categories of POIs and time availability. Our recommendation algorithm is largely inspired by the problem of sentence completion in natural language processing (NLP). Using a dataset of eight cities of different sizes, our experimental results demonstrate that our proposed algorithm is stable and outperforms many other sequence prediction algorithms, measured by recall, precision, and F1-scores.

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