Uncertainty-Aware Heterogeneous Representation Learning in POI Recommender Systems
Uncertainty-Aware Heterogeneous Representation Learning in POI Recommender Systems
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DOI：
10.1109/tsmc.2023.3252079
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Fan Zhou;Tangjiang Qian;Yuhua Mo;Zhangtao Cheng;Chunjing Xiao;Jin Wu;Goce Trajcevski
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文献类型：
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作者：
Fan Zhou;Tangjiang Qian;Yuhua Mo;Zhangtao Cheng;Chunjing Xiao;Jin Wu;Goce Trajcevski
发现有趣但未被访问的兴趣点(POI)是基于位置的社交网络(LBSN)中最实际的应用之一，但也是具有挑战性的问题。流行的方法面临几个问题，如数据稀疏性和难以对用户和POI之间的潜在非线性进行建模。此外，LBSN中的不确定性给学习用户的一般和当前兴趣的良好表示带来了额外的障碍。为了有效地解决这些问题，我们假设融合多个信息源是至关重要的。对此，我们提出了一种新颖的深度生成式推荐系统--Wasserstein自动编码器的POI推荐(WaPOIR)。它统一了来自用户个人偏好、社会影响力和地理数据的信息，并从历史签到中捕获用户的一般兴趣，同时根据最近访问的POI对用户的当前兴趣进行建模。与以前的方法不同，WaPOIR学习数据在Wasserstein空间中的潜在分布，作为LBSN中每个POI和每个用户的潜在表示。这使得能够同时维护社交和POI交互，并对其关系的不确定性进行建模。WaPOIR是一种随机推荐方法，允许对变量后验分布进行贝叶斯推断和近似。在真实的LBSN数据集上进行的大量实验表明，WaPOIR比最先进的方法获得了更好的性能。
Discovering interesting yet unvisited point-of-interests (POIs) is among the most practical applications but challenging problems in location-based social networks (LBSNs). Popular approaches face several issues, such as data sparsity and difficulties in modeling latent nonlinearity between users and POIs. Furthermore, the uncertainty in LBSNs poses additional obstacles to learning good representations of users’ general and current interests. To effectively address these issues, we postulate that fusing multiple sources of information is paramount. Toward that, we propose a novel deep generative recommender system—Wasserstein autoencoder for POI recommendation (WaPOIR). It unifies the information from users’ personal preference, social influence, and geographical data, and captures users’ general interests from historical check-ins, while modeling users’ current interests from recently visited POIs. Unlike previous methods, WaPOIR learns the latent distribution of data in the Wasserstein space as a potential representation for each POI and each user in LBSNs. This enables simultaneous maintenance of social and POI interactions and modeling the uncertainty of their relationships. WaPOIR is a stochastic recommendation approach that allows Bayesian inference and approximation of variational posterior distribution. Extensive experiments conducted on real-world LBSN datasets demonstrate that WaPOIR achieves better performance over the state-of-the-art approaches.