摘要:近些年来，兴趣点推荐系统已经逐渐成为移动推荐系统领域的研究热点之一.多种因素联合建模的方法逐渐深入，如时间、空间、序列、社会化和语义信息被引入统一模型，以建模多维情景下的用户偏好.其中，嵌入学习模型作为一种有效的多因素联合建模方法，在移动推荐领域有较好的性能.然而，多数嵌入学习的模型只是简单地将显式因素，如时间戳、项目、区域、序列等嵌入到相同的空间，由于缺乏对用户和项目的语义特征的深层次挖掘，在用户签到极端稀疏时，难以精准获取用户偏好.鉴于此，提出一种多维上下文感知的图嵌入模型——MCAGE.在MCAGE中，利用主题模型提取用户和项目间的潜在语义特征，并重新定义了一系列图的节点及关联规则，设计了更有效的用户偏好公式，以此提升刻画移动用户偏好的精准度.最后，通过在真实数据集上的实验分析，证明了该模型具有更好的推荐性能.



Abstract:In recent years, the point-of-interest (POI) recommendation system has gradually become one of the research hotspots in the field of mobile recommendation systems. The method of joint modeling of multiple factors, such as time, space, sequence, socialization, and semantic information, has been gradually introduced into a unified model to compute the user preferences under multidimensional scenarios. As an effective multi-factor joint modeling method, the embedding learning model has better performance in the mobile recommendation systems. However, many of the embedded learning models just simply embed the explicit factors, such as timestamps, items, regions, sequences, etc. into the same space. Due to the lack of deep mining of user and item semantic features, it is hard to accurately obtain user preferences when the users’ check-in data is extremely sparse. In view of this, a multi-dimensional context-aware graph embedding model, called MCAGE, is proposed in this study. In MACGE model, the topic model is used to extract the potential semantic features between users and items. Then, a series of graph nodes and association rules are redefined. To enhance the accuracy of describing the user preferences, a more effective user preference formula is designed. Finally, the results of experiments based on the real-world dataset shows that the proposed model has better recommendation performance.



PDF全文下载地址:

http://jos.org.cn/jos/article/pdf/5855