BTRec: BERT-Based Trajectory Recommendation for Personalized Tours
Ngai Lam Ho Roy Ka-Wei Lee Kwan Hui Lim
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.