Abstract
Point-of-Interest (POI) recommendation is one of the important services of location-based social networks (LBSNs), which has become an important way to help users discover interesting places and increase the potential income of related companies. Although human movement presents a sequential pattern in the LBSN. There still are the following problems: (1) when modeling the sequence data, most of the existing works assume that the check-in time depends on the location transformation in the location sequence. In particular, these works emphasize the equivalent transition probabilities between locations for all users to capture the check-in sequential pattern, whereas they ignore the spatial and temporal information of personalized context in some actual personal check-in scenarios; (2) most of the existing POI recommendation algorithms fail to utilize the social information related to modeling users to improve the final recommendation performance.To tackle the above challenges, we propose a new personalized successive POI recommendation model called Spatiotemporal Sequential and Social Embedding Rank model, named SSSER. First, we use a hybrid deep learning model based on the convolution filter and multilayer perceptron model to mine the sequence pattern among the users' checked-in locations. Then, we use the method of metric learning to model the social relationship among users. Finally, we propose a unified framework to recommend POIs combining the users' personal interests, the check-in sequential influence and social information simultaneously for the successive POI recommendation. And the BPR standard is used to optimize the loss function to fit the user's partial order of POIs. The experimental results on the real datasets show that our proposed POI recommendation algorithm outperforms the other state-of-the-art POI recommendation algorithms.
Highlights
With the rapid development of Web2.0, wireless communication and location collection technology have promoted many location-based social networks (LBSNs), such as Foursquare, Yelp, Facebook and so on
The spatiotemporal model utilize the Convolutional Neural Network(CNN) and Multilayer Perceptron(MLP) to capture potential features of check-ins matrix E(u,t) by user. our problem can be formally defined as generating a objective function y, based on the convolutional neural network, multilayer perceptron as well as the metric learning
We evaluate the recommendation quality of the SSSER by the four wide-use metrics. i.e., precision (Precision @ N) [48], recall (Recall @ N) [48], mean average precision (MAP) [48], and normalized depreciation cumulative gain [48]
Summary
With the rapid development of Web2.0, wireless communication and location collection technology have promoted many location-based social networks (LBSNs), such as Foursquare, Yelp, Facebook and so on. Most POI recommendation research work, like the literature [14]–[16], prefers to exploit the similarity of users’ social relationships based on the traditional collaborative filtering model to model users’ social links These methods make full use of a collaborative filtering model to mine users’ social links, but fail to achieve stable and reliable calculation results in the case where the number of common friends or common check-in information is scarce. The key contributions of this paper is as follows: 1) We propose a hybrid deep learning model based on a convolution filter [18] and a multi-layer perceptron model [19] to capture user preferences and the effects of spatiotemporal sequence patterns. We use the CNN method in the joint convolution filter based on image recognition to study these local features so as to capture the modeling of spatiotemporal sequence pattern effects on check-in locations and user preferences. A comprehensive experimental evaluation based on real-world datasets is conducted, which demonstrates that the proposed model is effective and is significantly superior to the state-of-the-art algorithms
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