Point-of-Interest Recommendation in Location Based Social Networks with Topic and Location Awareness
Point-of-Interest Recommendation in Location Based Social Networks with Topic and Location Awareness
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DOI：
10.1137/1.9781611972832.44
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发表时间：
2013
影响因子：
11.1
通讯作者：
B. Liu;Hui Xiong
中科院分区：
文献类型：
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作者：
B. Liu;Hui Xiong
基于位置的社交网络（LBSN）的广泛使用使得能够通过兴趣点（POI）推荐来实现更好的基于位置的服务的机会。实际上，POI推荐的问题是提供感兴趣的地方的个性化推荐。与传统的推荐任务不同，POI推荐是个性化的，位置感知的，并且依赖于上下文。针对这一差异，本文提出了一个主题和位置感知POI推荐系统，利用相关的文本和上下文信息。具体来说，我们首先利用聚合潜在狄利克雷分配（LDA）模型来学习用户的兴趣主题，并推断兴趣点相关的文本信息挖掘兴趣点。在此基础上，提出了一种基于主题和位置的概率矩阵分解方法（TL-PMF）。TL-PMF的独特视角是考虑用户兴趣在主题分布方面与POI匹配的程度以及POI的口碑意见。最后，对真实世界的LBSNs数据的实验表明，所提出的推荐方法优于国家的最先进的概率潜在因素模型具有显着的利润率。此外，我们还研究了个性化兴趣主题和口碑意见对POI推荐的影响。
The wide spread use of location based social networks (LBSNs) has enabled the opportunities for better location based services through Point-of-Interest (POI) recommendation. Indeed, the problem of POI recommendation is to provide personalized recommendations of places of interest. Unlike traditional recommendation tasks, POI recommendation is personalized, locationaware, and context depended. In light of this difference, this paper proposes a topic and location aware POI recommender system by exploiting associated textual and context information. Specifically, we first exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and to infer the interest POIs by mining textual information associated with POIs. Then, a Topic and Location-aware probabilistic matrix factorization (TL-PMF) method is proposed for POI recommendation. A unique perspective of TL-PMF is to consider both the extent to which a user interest matches the POI in terms of topic distribution and the word-of-mouth opinions of the POIs. Finally, experiments on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art probabilistic latent factor models with a significant margin. Also, we have studied the impact of personalized interest topics and word-of-mouth opinions on POI recommendations.