TGVx: Dynamic Personalized POI Deep Recommendation Model

Xiao-Jun Wang (), Tao Liu () and Weiguo Fan ()
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Xiao-Jun Wang: School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, China
Tao Liu: School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China
Weiguo Fan: Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, Iowa 52242

INFORMS Journal on Computing, 2023, vol. 35, issue 4, 786-796

Abstract: Personalized points-of-interest (POI) recommendation is very important for improving the service quality of location-based social network applications. It has become one of the most popular research directions in the industry and academia. However, the realization of high-quality personalized POI recommendation faces three major challenges: (i) the interest drift issue caused by the spatiotemporal dynamics of user check-in behavior, (ii) how to integrate as much heterogeneous information as possible to alleviate data sparseness and cold start issues, and (iii) how to use implicit feedback to model complex high-order nonlinear user-POI interactions. To jointly address all these challenges, we propose the TGVx recommendation framework and establish a dynamic personalized POI deep recommendation model, where T and G respectively represent time and geographic factors, V represents out-of-town visitors, and x represents time slot number. TGVx is composed of x parallel TGV models where the TG module mines high-order nonlinear user-POI interaction relationships and integrates multisource heterogeneous information, and the V module transfers the check-in records of out-of-town visitors in hometowns and generates pseudo check-in records in the target city. Technically, we design a new unsupervised deep learning network T-SemiDAE for the TG module. We built a POI-word heterogeneous network for the V module and used graph embedding technology to match the most similar POIs across cities and transfer check-in records. The experimental results on the actual datasets show that the TGVx model is always better than other advanced models in terms of accuracy and diversity for local and out-of-town recommendation scenarios. Compared with the best baseline model semi-deep auto-encoder with a conditional layer the average improvement rates of accuracy and diversity of TGVx are 17.1% to 58.6% and 2.25% to 28.86%, respectively. In theory, our research effectively uses data science and analysis methods to design a recommender system. In practice, our research is motivated by practical problems, and the research results have high practical promotion value.

Keywords: location-based social network; personalized point-of-interest recommendation; deep learning; graph embedding representation; out-of-town recommendation; spatio-temporal dynamics (search for similar items in EconPapers)
Date: 2023
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