Gated Spectral Units: Modeling Co-evolving Patterns for Sequential Recommendation

Exploiting historical data of users to make future predictions lives at the heart of building effective recommender systems (RS). Recent approaches for sequential recommendations often render past actions of a user into a sequence, seeking to capture the temporal dynamics in the sequence to predict the next item. However, the interests of users evolve over time together due to their mutual influence, and most of existing methods lack the ability to utilize the rich coevolutionary patterns available in underlying data represented by sequential graphs. In order to capture the co-evolving knowledge for sequential recommendations, we start from introducing an efficient spectral convolution operation to discover complex relationships between users and items from the spectral domain of a graph, where the hidden connectivity information of the graph can be revealed. Then, the spectral convolution is generalized into an recurrent method by utilizing gated mechanisms to model sequential graphs. Experimentally, we demonstrate the advantages of modeling co-evolving patterns, and Gated Spectral Units (GSUs) achieve state-of-the-art performance on several benchmark datasets.