【摘要】 在基于位置的社交网络(Location-based Social Networks, LBSN)中,用户共享位置和与位置信息相关的内容。兴趣点推荐是LBSN的重要应用,根据用户历史访问签到记录推荐其可能感兴趣的位置。与其他推荐问题(如产品推荐或电影推荐)相比,用户对兴趣点的偏好在时间感知特征上尤为凸显。文中探索了时间感知特征对兴趣点推荐任务的影响,提出了时间感知的兴趣点推荐方法TAPR(Time Aware POI Recommendation)。该算法基于不同的时间尺度构建不同的关系矩阵,并且利用张量分解将构建出的多个关系矩阵分解从而得到用户与兴趣点的表示。最后,该算法利用余弦相似性计算用户与未访问POIs的相似性得分,并结合用户偏好建模的算法得到最终推荐分数。在两个公开数据集上的实验结果表明,TAPR算法比其他基于兴趣点推荐算法表现更好。更多还原

【Abstract】 In location-based social networks(LBSN),users share their location and content related to location information.Point-of-interest(POI) recommendation is an important application in LBSN which recommends locations that might be of interest to users.However, compared with other recommendation problems(such as product and movie recommendation),the users’ prefe-rence for POI is particularly determined by the time feature.In this paper, the influence of time feature on POI recommendation task is explored, and a time-aware POI recommendation method is proposed, called TAPR(Time Aware POI Recommendation).Our method constructs different relation matrices based on different time scales, and uses tensor decomposition to decompose the constructed multiple relation matrices to obtain the representation of the user and the POI.Finally, our method uses cosine similarity to calculate similarity scores between users and non-visited POIs, and combines the algorithm of user preference modeling to obtain the final recommendation score.Experimental results on two public datasets show that the proposed TAPR performs better than other POI recommendation methods.更多还原