基于用户关系网络表征学习的服务推荐方法

网络嵌入学习是深度学习的一个热门分支,它将网络节点映射到一个拓展的低维向量空间。针对用户共用标签网络和社交网络,利用表征学习方法得到用户标签标注关系和社交关系的向量表征,并提出一种新的服务推荐方法。该方法利用用户的向量表征得到相似用户集,由最终得到的用户特征信息返回Top-k个相似用户,并根据相似用户的偏好情况向目标用户推荐合适的服务。为验证方法的可行性,在公开数据集Delicious和Last.FM上进行了实验,结果表明:相比4种基准方法,文中方法准确率可提升13%,召回率提升18.6%,F-measure值可提升13.1%;在学习用户表征向量时,用户之间共用标签关系与社交关系同样重要;推荐过程中,为目标用户返回的相似用户值在[25,30]区间更为适宜。

Service recommendation based on user network representation learning

Network embedding has been a popular branch of deep learning, which represents the network information by mapping network nodes to an extended low-dimensional space. According to user co-tag network and social network, we employed network representation learning to extract the representation vector of tagging relationship and social relationship of users respectively, and proposed a novel service recommendation method, using user''s representation vector learned to calculate the similar user set and recommend appropriate services to the target users according to the preferences of top-k similar users. To investigate the feasibility of our approach, experiments were carried out on two open data sets, Delicious and Last.FM. The results show that our method outperforms the four benchmarks, with an average improvement of 13% in precision, 18.6% in recall and 13.1% in F-measure. It is also found that when learning user representation, the co-tag relationship between users is as important as the social relationship. Meanwhile, during the process of collaborative recommendation, the number of similar users returned for a target user is suitable in the range from 25 to 30.