DLAN:Modeling user long- and short-term preferences based on double-layer attention network for next point-of-interest recommendation

Author:

Wu Yuhang12,Jiao Xu34,Hao Qingbo12,Xiao Yingyuan12,Zheng Wenguang12

Affiliation:

1. Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin, China

2. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China

3. School of General Education, Tianjin Foreign Studies University, Tianjin, China

4. Department of Computer Science, Norwegian University of Science and Technology, Norway

Abstract

The next Point-of-Interest (POI) recommendation, in recent years, has attracted an extensive amount of attention from the academic community. RNN-based methods cannot establish effective long-term dependencies among the input sequences when capturing the user’s motion patterns, resulting in inadequate exploitation of user preferences. Besides, the majority of prior studies often neglect high-order neighborhood information in users’ check-in trajectory and their social relationships, yielding suboptimal recommendation efficacy. To address these issues, this paper proposes a novel Double-Layer Attention Network model, named DLAN. Firstly, DLAN incorporates a multi-head attention module that can combine first-order and high-order neighborhood information in user check-in trajectories, thereby effectively and parallelly capturing both long- and short-term preferences of users and overcoming the problem that RNN-based methods cannot establish long-term dependencies between sequences. Secondly, this paper designs a user similarity weighting layer to measure the influence of other users on the target users leverage the social relationships among them. Finally, comprehensive experiments are conducted on user check-in data from two cities, New York (NYC) and Tokyo (TKY), and the results demonstrate that DLAN achieves a performance in Accuracy and Mean Reverse Rank enhancement by 8.07% -36.67% compared to the state-of-the-art method. Moreover, to investigate the effect of dimensionality and the number of heads of the multi-head attention mechanism on the performance of the DLAN model, we have done sufficient sensitivity experiments.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference2 articles.

1. Personalized long-and short-term preference learning for next POI Recommendation,Trans. Knowl. Data Eng.;Yuxia Wu;IEEE,2022

2. Modeling user activity preference by leveraging user spatialtemporal characteristics in lbsns;Dingqi Yang;IEEE Transactions onSystems, Man, and Cybernetics: Systems,2015

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