Leveraging contextual influence and user preferences for point-of-interest recommendation
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Leveraging contextual influence and user preferences for point-of-interest recommendation Dongjin Yu1 · Wenbo Wanyan1 · Dongjing Wang1 Received: 3 June 2020 / Revised: 20 August 2020 / Accepted: 26 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The effective Point-of-Interest (POI) recommendation can significantly assist users to find their preferred POIs and help POI owners to attract more customers. As a result, a variety of methods have been proposed to tackle the issue of POI recommendation recently. However, it is still very difficult to precisely model the strong correlations between the POIs visited by the user and the POIs to be visited next, which leads to the poor performance of POI recommendation. In this paper, we propose a context- and preference- aware model (CPAM) to incorporate both contextual influence and user preferences into POI recommendation. Firstly, we design a Skip-Gram based POI Embedding Model (SG-PEM) to capture the contextual influence of POIs and learn the vector representation (embedding) of POIs from visiting sequences. The users’ preferences for the target POIs are obtained from the learned embeddings via similarity metric. Secondly, for the implicit feedback information contained in the check-in data, we use the Logistic Matrix Factorization (LMF) algorithm to model the users’ personalized preferences for POI. Finally, we unify SG-PEM and LMF as the CPAM model to perform personalized recommendation by leveraging contextual influence and user preferences. The experimental results on two real-world datasets of Foursquare and Gowalla show that the proposed model outperforms the state-of-the-art baselines. Keywords Recommender system · POI recommendation · POI embedding · Matrix factorization
This research is supported by Zhejiang Provincial Natural Science Foundation of China under No. LQ20F020015, and the Fundamental Research Funds for the Provincial University of Zhejiang under No. GK199900299012-017. Dongjing Wang
[email protected] Dongjin Yu [email protected] Wenbo Wanyan [email protected] 1
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
Multimedia Tools and Applications
1 Introduction With the development of GPS-enabled smartphones and mobile internet, users can easily obtain real-time geographic location information, and record/share their activities and daily lives via check-in on Location Based Social Networks (LBSN), such as Yelp, Instagram, Foursquare and Twitter. Specifically, as the digital mirror to human trajectories in physical world, these POI check-in sequences on LBSN indicate human mobility and behavioral information, including sequential context or periodic transition patterns [16, 44]. For example, people may regularly stop by coffee shop to grab a cup of coffee on their way to work in the morning, and some users may prefer to have high-protein dinner after taking part in sports and physical activity, which can be explained as sequential transition patterns.
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