Tag embedding based personalized point of interest recommendation system

Abstract

E-tourism websites such as Foursquare, Tripadvisor, Yelp etc. allow users to rate the preferences for the places they have visited. Along with ratings, the services allow users to provide reviews on social media platforms. As the use of hashtags has been popular in social media, the users may also provide hashtag-like tags to express their opinion regarding some places. In this article, we propose an embedding based venue recommendation framework that represents Point Of Interest (POI) based on tag embedding and models the users (user profile) based on the POIs rated by them. We rank a set of candidate POIs to be recommended to the user based on the cosine similarity between respective user profile and the embedded representation of POIs. Experiments on TREC Contextual Suggestion data empirically confirm the effectiveness of the proposed model. We achieve significant improvement over PK-Boosting and CS-L2Rank, two state-of-the-art baseline methods. The proposed methods improve NDCG@5 by 12.8%, P@5 by 4.4%, and MRR by 7.8% over CS-L2Rank. The proposed methods also minimize the risk of privacy leakage. To verify the overall robustness of the models, we tune the model parameters by discrete optimization over different measures (such as AP, NDCG, MRR, recall, etc.). The experiments have shown that the proposed methods are overall superior than the baseline models.