RecNet: a deep neural network for personalized POI recommendation in location-based social networks

ABSTRACT

How to exploit various features of users and points of interest (POIs) for accurate POI recommendation is important in location-based social networks (LBSNs). In this paper, a novel POI recommendation framework, named RecNet, is proposed, which is developed based on a deep neural network (DNN) to incorporate various features in LBSNs and learn their joint influence on user behavior. More specifically, co-visiting, geographical and categorical influences in LBSNs are exploited to alleviate the data sparsity issue in POI recommendation and are converted to feature vector representations of POIs and users via feature embedding. Moreover, the embedded POIs and users are fed into a DNN pairwise to adaptively learn high-order interactions between features. Our method is evaluated on two publicly available LBSNs datasets and experimental results show that RecNet outperforms state-of-the-art algorithms for POI recommendation.

KEYWORDS:

• POI recommendation
• location-based social networks
• deep neural network
• feature embedding

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://foursquare.com/.

2. https://www.yelp.com/.

Additional information

Funding

This work was supported in part by grants from the National Key R&D Program of China [No.2017YFB1002202] and the Fundamental Research Funds for the Central Universities.