Pair-wise ranking based preference learning for points-of-interest recommendation

Abstract Recommending point-of-interest (POI) to users accurately is a hot topic in business. In the past, many researchers proposed recommendation models based on collaborative filtering or matrix factorization from the perspectives of time, geography, and social relationship. However, only a few studies have focused on user preference which is the key factor influencing user decision. This work focuses on studying the representation and mining of user preference from check-in data for POI recommendation. Pair-wise ranking is the common solution for implementing preference learning. However, traditional ways of constructing pair-wise data cut off the connections between multiple options in the decision process, affecting the effectiveness of preference learning. In this work, we change the ratio of negative to positive instance in pair-wise data from 1:1 to k:1 to ensure the data construction in line with the real decision making process. We propose a new negative sampling method taking the geographical distance and POI categorical distance into consideration jointly for enhancing the quality of training data. For our specialized pair-wise data, we propose a new optimization criterion for implementing effective preference learning. Finally, we conduct extensive experiments on two real-world datasets to validate the effectiveness of our proposed approach. The experiment results show that our approach outperforms the state-of-the-art models by at least 19.7% on F1-Score and 24.4% on nDCG. Additionally, our approach can be easily generalized to other domains, such as commodities, news, and movie recommendation.