融合社交关系与时间因素的主题模型推荐算法  

Topic Model Recommendation Algorithm Combining Social Relationship and Time Factors

作　　者：高茂庭[1] 王吉 GAO Maoting;WANG Ji(College of Information Engineering,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学信息工程学院,上海201306

出　　处：《计算机工程》2020年第3期66-72,共7页Computer Engineering

基　　金：国家自然科学基金(61202022)。

摘　　要：用户的行为偏好往往会受到社交关系、时间变化等多种因素影响,只考虑单一因素会导致构建的用户兴趣模型比较片面,难以准确地产生推荐。为此,融合用户社交关系和时间因素,提出一种主题模型推荐算法。利用主题模型对用户标注行为进行主题建模,得到用户-物品概率矩阵。根据用户标注物品的时间计算用户标注行为的时间权重,将其与用户的标注行为权重相结合,计算基于时间的用户相似度。对用户的社交关系与基于时间的用户相似度进行加权处理得到用户的权重,在此基础上,考虑其他用户的影响,计算用户对物品最终的偏好权重,并根据排名产生推荐结果。在Last.fm数据集上的实验结果表明,该算法能更全面地考虑用户特征,有效提高推荐的质量。The user behavior preference is often affected by many factors such as social relationships,time and so on.However,when constructing the user preference model,if only one single factor is considered,the model can be one-sided,resulting in incorrect recommendations.Therefore,this paper proposes a topic model recommendation algorithm,in which the user social relationships and time factors are considered.The topic model is used to model the user’s labeling behavior,thus obtaining the user-item probability matrix.According to the time spent on labeling the items,the time weight of user’s labeling behavior is calculated,and combined with the user’s labeling behavior weight to calculate the time-based user similarity.Then,the user’s weight is obtained by weighting the user’s social relationships and time-based user similarity.On this basis,with a consideration of other user’s influence,the finally preference weight of user-item is calculated and the recommendation results is obtained by preference ranking.Experimental results on the Last.fm dataset show that the proposed algorithm can consider the user’s features in a more comprehensive way,thus improving the recommendation quality.

关 键 词：社交关系 标签行为 时间权值 主题模型 推荐算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]