POI Recommendation of Location-Based Social Networks Using Tensor Factorization
POI Recommendation of Location-Based Social Networks Using Tensor Factorization
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DOI：
10.1109/mdm.2018.00028
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Guoqiong Liao;Shan Jiang;Zhiheng Zhou;Changxuan Wan;X. Liu
中科院分区：
文献类型：
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作者：
Guoqiong Liao;Shan Jiang;Zhiheng Zhou;Changxuan Wan;X. Liu
随着无线通信技术的快速发展，基于位置的社交网络（LBSNs）如Foursquare和Gowalla已经变得非常流行。兴趣点推荐是LBSN中一种重要的增强用户体验的推荐方法。与在线社交网络不同，LBSN拥有大量的签到数据和评论信息，可以为POI推荐提供有价值的信息。为了提高兴趣点推荐的准确率，提出了一种基于张量分解的推荐策略。该方法首先利用潜在狄利克雷分配（LDA）主题模型提取主题信息，并根据用户的评论信息生成每个兴趣点的主题概率分布。其次，将每个用户的签到数据划分为对应于一天中每个小时的多个数据切片。通过连接每个用户访问兴趣点的主题分布，用户主题时间张量来呈现所有用户的潜在偏好。最后，采用高阶奇异值分解（HOSVD）算法对三阶张量进行分解，得到稠密的兴趣点偏好信息。在一个真实的数据集上的实验表明，该方法比基准方法具有更好的性能。
With the rapid development of wireless communication technologies, location-based social networks (LBSNs) like foursquare and Gowalla have become very popular. Point of interest (POI) recommendation is a kind of important recommendation in LBSNs for enhancing user experiences. Unlike online social networks, LBSNs have a great deal of check-in data and comment information, which can provide valuable information for POI recommendation. In this paper, a novel recommendation strategy using tensor factorization is proposed for improving accurate rate of POI recommendation. Firstly, the latent dirichlet allocation(LDA) topic model is used to extract topic information and generate topic probability distribution of each POI based on comment information from users. Secondly, the check-in data of each user is divided into multiple data slices corresponding to each hour of a day. By connecting with the topic distributions of the visited POIs of each user, a user-topic-time tensor is conducted to present the potential preferences of all users. Finally, a higher order singular value decomposition (HOSVD) algorithm is employed to decompose the third-order tensor, to get dense preference information for POI recommendation. The experiments on a real dataset show that the proposed approach have better performance than the baseline methods.