FIRE: Fast Incremental Recommendation with Graph Signal Processing
Jiafeng Xia, Dongsheng Li, Hansu Gu, Jiahao Liu, Tun Lu, Ning Gu
Abstract
Recommender systems are incremental in nature. 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/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 FIRE, 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 accuracy by a large margin and improve the model updating efficiency by at least 3X compared with the state-of-the-art incremental recommendation algorithms. The Code is available at https://github.com/Yaveng/FIRE.