基于异构邻域聚合的协同过滤推荐算法

Collaborative Filtering with Heterogeneous Neighborhood Aggregation

传统的协同过滤算法使用one-hot编码生成的特征向量信息量稀少,对异构行为数据仅挖掘不同行为间的联系而忽略用户间行为的联系.针对上述问题,文中提出基于异构邻域聚合的协同过滤推荐算法.首先,使用图对用户和项目的异构交互进行建模,并利用图的连通特性构建邻域.然后,使用轻量级图卷积方法整合邻域信息,融入目标用户和目标项目的特征向量.最后,将融合邻域信息的用户与项目的特征向量输入多任务异构网络进行训练,通过丰富特征向量信息的方法缓解数据稀疏问题.在数据集上的实验证实文中算法的性能较优.

In traditional collaborative filtering models,the feature vector generated by one-hot encoding is sparsely informative.Heterogeneous behavior data is only employed to describe the relationship between different behaviors and the relationship between behaviors of different users is ignored.Aiming at these problems,an algorithm of collaborative filtering with heterogeneous neighborhood aggregation is proposed.Firstly,the heterogeneous interaction between users and items is modeled by the graph,and neighborhoods are built through the connectivity of graph.Then,the neighborhood information integrated by the lightweight graph convolution method is merged into the feature vectors of the target users and items.Finally,the feature vectors of users and items integrating with neighborhood information are input into a multi-task heterogeneous network for training.The problem of data sparseness is alleviated by enriching the hidden information of feature vectors.Experiments on the datasets prove that the performance of the proposed model is better.