734 research outputs found
POIBERT: A Transformer-based Model for the Tour Recommendation Problem
Tour itinerary planning and recommendation are challenging problems for
tourists visiting unfamiliar cities. Many tour recommendation algorithms only
consider factors such as the location and popularity of Points of Interest
(POIs) but their solutions may not align well with the user's own preferences
and other location constraints. Additionally, these solutions do not take into
consideration of the users' preference based on their past POIs selection. In
this paper, we propose POIBERT, an algorithm for recommending personalized
itineraries using the BERT language model on POIs. POIBERT builds upon the
highly successful BERT language model with the novel adaptation of a language
model to our itinerary recommendation task, alongside an iterative approach to
generate consecutive POIs.
Our recommendation algorithm is able to generate a sequence of POIs that
optimizes time and users' preference in POI categories based on past
trajectories from similar tourists. Our tour recommendation algorithm is
modeled by adapting the itinerary recommendation problem to the sentence
completion problem in natural language processing (NLP). We also innovate an
iterative algorithm to generate travel itineraries that satisfies the time
constraints which is most likely from past trajectories. Using a Flickr dataset
of seven cities, experimental results show that our algorithm out-performs many
sequence prediction algorithms based on measures in recall, precision and
F1-scores.Comment: Accepted to the 2022 IEEE International Conference on Big Data
(BigData2022
- …