Multi-channel Orthogonal Decomposition Attention Network for Sequential Recommendation

Sequential recommender systems aim to model users’ evolving interests from historical behaviors and make customized recommendations. Except for items, the feature carried by the interaction also contains a wealth of information (e.g., item category and user rating). Therefore, many researches tried to leverage features, which directly fuse various types of features into the item vector. However, items and features are in different vector spaces, so the direct fusion destroys the consistency of the item vector space. Furthermore, the direct fusion of multiple features leads to mutual interference, making it hard to capture the transfer patterns of feature sequences. In this paper, we propose a novel Multi-channel Orthogonal Decomposition Attention Network (MODAN) for the sequential recommendation. Specifically, we apply two kinds of channels. One is the item channel, which only focuses on the pure dependency among items. The other is the feature channel, which captures the feature transfer patterns. In the feature channels, we adopt orthogonal decomposition and reverse orthogonal decomposition to maintain the consistency of both the item and feature vector space. Experimental results on three datasets demonstrate that MODAN achieves substantial improvement over state-of-the-art methods.