Real-world recommender systems are incremental in nature, in which new users, items and user-item interactions are observed continuously over time. Recent progresses in incremental recommendation rely on capturing the temporal dynamics of users\/items from temporal interaction graphs, so that their user\/item embeddings can evolve together with the graph structures. However, these methods are faced with two key challenges: 1) model training and\/or updating are time-consuming and 2) new users and items cannot be effectively handled. To this end, we propose the fast incremental recommendation (FIRE) method from a graph signal processing perspective. FIRE is non-parametric which does not suffer from the time-consuming back-propagations as in previous learning-based methods, significantly improving the efficiency of model updating. In addition, we encode user\/item temporal information and side information by designing new graph filters in the proposed method, which can capture the temporal dynamics of users\/items and address the cold-start issue for new users\/items, respectively. Experimental studies on four popular datasets demonstrate that FIRE can improve the recommendation accuracy…<\/p>\n","protected":false},"excerpt":{"rendered":"
Real-world recommender systems are incremental in nature, in which new users, items and user-item interactions are observed continuously over time. Recent progresses in incremental recommendation rely on capturing the temporal dynamics of users\/items from temporal interaction graphs, so that their user\/item embeddings can evolve together with the graph structures. However, these methods are faced with 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