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Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation

2019-05-19 18:00:05
Xiao Zhou, Cecilia Mascolo, Zhongxiang Zhao

Abstract

Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry. Despite their value, however, they still suffer from the challenges of capturing complicated user preferences and fine-grained user-POI relationship for spatio-temporal sensitive POI recommendation. Existing recommendation algorithms, including both shallow and deep approaches, usually embed the visiting records of a user into a single latent vector to model user preferences: this has limited power of representation and interpretability. In this paper, we propose a novel topic-enhanced memory network (TEMN), a deep architecture to integrate the topic model and memory network capitalising on the strengths of both the global structure of latent patterns and local neighbourhood-based features in a nonlinear fashion. We further incorporate a geographical module to exploit user-specific spatial preference and POI-specific spatial influence to enhance recommendations. The proposed unified hybrid model is widely applicable to various POI recommendation scenarios. Extensive experiments on real-world WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and 29.95% for context-aware and sequential recommendation, respectively). Also, qualitative analysis of the attention weights and topic modeling provides insight into the model's recommendation process and results.

Abstract (translated)

兴趣点(POI)推荐系统通过向用户推荐未经探索的POI,在人们的生活中起着至关重要的作用,引起了学术界和业界的广泛关注。然而,尽管它们有价值,它们仍然面临着捕获复杂的用户偏好和细粒度的用户POI关系以获得时空敏感的POI建议的挑战。现有的推荐算法,包括浅层和深层方法,通常将用户的访问记录嵌入到一个单一的潜在向量中,以模拟用户偏好:这限制了表示和解释的能力。在本文中,我们提出了一种新颖的主题增强记忆网络(temn),它是一种深入的体系结构,以非线性方式利用潜在模式的全局结构和基于局部邻域的特征,将主题模型和记忆网络集成在一起。我们还结合了一个地理模块,利用用户特定的空间偏好和POI特定的空间影响来增强建议。所提出的统一混合模型广泛适用于各种POI推荐方案。对真实微信数据集进行的大量实验证明了其有效性(上下文感知和顺序推荐的改善率分别为3.25%和29.95%)。此外,对注意力权重和主题建模的定性分析可以深入了解模型的推荐过程和结果。

URL

https://arxiv.org/abs/1905.13127

PDF

https://arxiv.org/pdf/1905.13127.pdf


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