Exploring timeliness for accurate recommendation in location-based social networks
Exploring timeliness for accurate recommendation in location-based social networks
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DOI：
10.3934/mfc.2018002
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Yi Xu;Qing Yang;Dianhui Chu
中科院分区：
文献类型：
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作者：
Yi Xu;Qing Yang;Dianhui Chu
个体在真实的世界中的位置历史暗示了他或她的兴趣和行为。本文分析和理解的过程中，协同过滤（CF）的方法，挖掘个人的偏好，从他/她的地理位置的历史和推荐位置的基础上，用户和其他人之间的相似性。我们发现，基于CF的推荐过程可以概括为一个序列的乘法之间的转移矩阵和定位矩阵。转移矩阵通常由用户的兴趣矩阵来近似，该兴趣矩阵反映了用户之间关于他们访问不同位置的兴趣的相似性。已验证位置矩阵提供所有用户的访问位置的历史，其当前可用于推荐系统。我们发现，当且仅当转移矩阵保持不变时，推荐结果才会收敛;否则，推荐仅在一定时间内有效。在此基础上，提出了一种新的基于位置的精确推荐（LAR）方法，该方法综合考虑了位置的语义和类别信息，以及推荐结果的时效性，从而实现精确推荐。我们评估了准确率和召回率的LAR，使用大规模的真实世界的数据集收集的Remarkkite。评估结果证实，LAR提供更准确的建议，与最先进的方法相比。
An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.