基于相似用户聚类和张量分解的上下文感知兴趣点推荐

Yan Zhou, Kaixuan Zhou, Shuaixian Chen
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引用次数: 0

摘要

大数据技术和移动智能设备的快速发展带动了基于位置的社交网络的发展。为了了解用户的行为模式,提高定位服务的准确性,兴趣点推荐成为一项重要的任务。与一般的产品推荐任务相比,POI推荐面临用户签入数据的稀疏性和弱语义问题。为了解决这些问题,越来越多的研究通过引入地理、时间、文本和社会关系等上下文信息来提高POI建议的准确性。然而,丰富的上下文也给POI推荐带来了巨大的挑战,如上下文信息的利用率低、上下文信息的丰富度难以平衡、推荐矩阵的复杂性等。考虑到相似用户比一般用户有更多共同的兴趣偏好,相似用户的签到信息具有更大的参考意义。因此,我们提出了一种个性化的POI推荐方法CULT-TF,该方法将相似用户的上下文信息纳入张量分解模型。首先,我们提出了用户活动模型和用户相似度模型,结合上下文信息计算用户活动和用户之间的相似度。根据用户活跃度选择最具代表性的活跃用户作为用户聚类中心,然后根据用户相似度将用户聚类为几个相似的用户聚类(C)。接下来,我们使用用户活跃度、POI流行度和时点流行度作为用户(U)、位置(L)和时间(T)维度的特征值,为每个用户聚类构建一个三阶张量(用户-位置-时间矩阵)。通过整合用户在用户、地点和时间层面的签到行为的上下文信息,对每个维度的特征值进行建模。相似用户聚类减少了张量建模中的用户数量,降低了U维。为了进一步降低推荐矩阵的复杂度,通过ROI (region of interest)聚类实现L维的降维,通过时隙编码实现T维的降维。然后,我们使用张量分解(TF)来获得推荐结果。该方法降低了张量矩阵的复杂度,并集成了用户签入行为的丰富上下文信息。最后,我们使用来自Brightkite的真实LBSN数据集对CULT-TF进行了全面的性能评估。实验结果表明,该方法在准确率和召回率方面都优于其他推荐方法。
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Context-Aware Point-of-Interest Recommendation Based on Similar User Clustering and Tensor Factorization
The rapid development of big data technology and mobile intelligent devices has led to the development of location-based social networks (LBSNs). To understand users’ behavioral patterns and improve the accuracy of location-based services, point-of-interest (POI) recommendation has become an important task. In contrast to the general task of product recommendation, POI recommendation faces the problems of the sparsity and weak semantics of user check-in data. To address these issues, an increasing number of studies have improved the accuracy of POI recommendations by introducing contextual information such as geographical, temporal, textual, and social relations. However, the rich context also brings great challenges to POI recommendation, such as the low utilization rate of context information, difficulty in balancing the richness of contextual information, and the complexity of the recommendation matrix. Considering that similar users have more interest preferences in common than users generally have, the check-in information of similar users has greater reference meaning. Thus, we propose a personalized POI recommendation method named CULT-TF, which incorporates similar users’ contextual information into the tensor factorization model. First, we present a user activity model and a user similarity model, which integrate contextual information to calculate the user activity and similarity between users. According to user activity, the most representative active users are selected as user clustering centers, and then users are clustered based on user similarity into several similar user clusters (C). Next, we construct a third-order tensor (user-location-time matrix) for each user cluster by using user activity, POI popularity, and time slot popularity as the eigenvalues in the user (U), location (L), and time (T) dimensions, and the eigenvalue of each dimension is modeled by integrating contextual information of users’ check-in behavior at the user, location, and time levels. Similar user clustering reduces the number of users in tensor modeling, reducing the U dimension. To further reduce the complexity of the recommendation matrix, the reduction of the L dimension is achieved through ROI (region of interest) clustering, and the reduction of the T dimension is achieved through time slot encoding. Then, we use tensor factorization (TF) to obtain the recommendation results. Our method decreases the complexity of the tensor matrix and integrates rich contextual information on users’ check-in behavior. Finally, we conducted a comprehensive performance evaluation of CULT-TF using real-world LBSN datasets from Brightkite. The experimental results show that our proposed method performs much better than other recommendation methods in terms of precision and recall.
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