AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sijia Liu Jiahao Liu Hansu Gu Dongsheng Li Tun Lu Peng Zhang Ning Gu
Sequential recommendation demonstrates the capability to recommend items by
modeling the sequential behavior of users. Traditional methods typically treat
users as sequences of items, overlooking the collaborative relationships among
them. Graph-based methods incorporate collaborative information by utilizing
the user-item interaction graph. However, these methods sometimes face
challenges in terms of time complexity and computational efficiency. To address
these limitations, this paper presents AutoSeqRec, an incremental
recommendation model specifically designed for sequential recommendation tasks.
AutoSeqRec is based on autoencoders and consists of an encoder and three
decoders within the autoencoder architecture. These components consider both
the user-item interaction matrix and the rows and columns of the item
transition matrix. The reconstruction of the user-item interaction matrix
captures user long-term preferences through collaborative filtering. In
addition, the rows and columns of the item transition matrix represent the item
out-degree and in-degree hopping behavior, which allows for modeling the user's
short-term interests. When making incremental recommendations, only the input
matrices need to be updated, without the need to update parameters, which makes
AutoSeqRec very efficient. Comprehensive evaluations demonstrate that
AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing
its robustness and efficiency.