基于双层网络结构综合考虑社交关系与偏好相似性的电影推荐方法

A Movie Recommendation Method Considering Social Relationships and Preference Similarity Based on A Double-Layer Network Structure

当前电影推荐方法缺乏对用户间多种关联关系的考虑,导致用户好友关系稀疏,推荐效果不佳。为此,本文基于双层网络结构提出一种综合考虑社交关系与偏好相似性的电影推荐方法。首先,根据不同关系类型对电影网站用户社交关系和偏好相似关系进行分层处理,通过用户间的关注关系构造社交网络。接着,基于用户评价电影集合的相似性度量用户偏好相似性,构造偏好相似网络,继而构建综合考虑用户社交关系与偏好相似关系的双层网络结构。最后,利用Louvain社区发现方法对大群体用户进行降维分析,基于邻居用户影响力对社群用户进行个性化推荐。经豆瓣数据实验分析,本文所提方法能够有效缓解用户社交关系稀疏性问题,高效识别用户感兴趣社群,并基于社区邻居评价信息为目标用户提供更具新颖度的推荐列表。

The research on movie recommendation algorithms developed rapidly because of the generation of new technologies such as Web 2.0,Big data,and cloud computing.Most recommendation systems operate based on preference similarity or social distance.However,current movie recommendations still face sparse issues on item ratings and social relationships.According to sociological theory,preference similarity is one of the important factors in the formation of social relationships,and social relationships also contribute to the emergence of similar preferences.Thus,considering multiple association relationships between users and the interaction between these relationships is the key to accurately capturing user needs and improving recommenda⁃tion accuracy.A double-layer network-based recommendation model is proposed that simultaneously considers users’social relationships and preference similarity.The social network is obtained based on the"follow"relationship and the preference similarity network is built based on similar reviews.Based on the single-layer processing of the double-layer network,the Louvain method is used to reduce the dimension of a large group of users.Then,the influence of intra-class users is calculated based on multi-level relationships and the personal⁃ized recommendation is provided for target users.Three main contributions are made by this paper.Firstly,the sparsity problem of users’evaluation of the target movie is improved by extending the target movie to a collec⁃tion of similar movies.Secondly,the sparsity of users’social relationships is alleviated by preference similar⁃ity.Thirdly,considering the double-layer relationships,the needs of target users and recommendation lists can be accurately predicted.This paper takes Douban data(including 384548 users,269469 movies,and the Top 250 movie list in 2022)as an example to verify the effectiveness of the recommendation model.Through experimental analysis,the sparsity problem of user relationships is effectively alleviated,the communities that users are interested in are efficiently identified,and more novel items are recommended to target users.Additionally,this method is also suitable for predicting missing information and identifying similar preference communities,providing data support for a more comprehensive understanding of user preferences.