文献类型：期刊文献

英文题名：Attention-based dynamic user modeling and Deep Collaborative filtering recommendation

作者：Wang, Ruiqin[1,3];Wu, Zongda[2];Lou, Jungang[1,3];Jiang, Yunliang[1,3]

机构：[1]Huzhou Univ, Sch Informat Engn, Huzhou, Peoples R China;[2]Shaoxing Univ, Sch Mech & Elect Engn, Shaoxing, Peoples R China;[3]Zhejiang Prov Key Lab Smart Management & Applicat, Huzhou, Peoples R China

年份：2022

卷号：188

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：SCI-EXPANDED(收录号：WOS:000768193500016)、、EI(收录号：20214211032917)、Scopus(收录号：2-s2.0-85117156707)、WOS

基金：This work is supported by the Chinese National Funding of Social Sciences (No. 20BTQ093).

语种：英文

外文关键词：Short-term preferences; Long-term preferences; Dynamic preference modeling; Matching score prediction; Time-aware attention

外文摘要：Deep learning (DL) techniques have been widely used in recommender systems for user modeling and matching function learning based on historical interaction matrix. However, existing DL-based recommendation methods usually perform static user preference modeling by using historical interacted items of the user. In this article, we present a time-aware deep CF framework which contains two stages: dynamic user preference modeling based on attention mechanism and matching score prediction based on DL. In the first stage, short-term user preferences are modeled by the time-aware attention mechanism that fully considered the predicted item, the recent interacted items and their interaction time. The resulting short-term preferences are combined with long-term preferences for dynamic user preference modeling. In the second stage, high-order user-item feature interactions are learned by two types of DL models, Deep Matrix Factorization (DMF) and Multiple-Layer Perception (MLP), and the feature interaction vectors of the two models are fused in the last layer of the model to predict the matching score. Extensive experiments on five datasets indicate that our method is superior to the existing time-aware and DL-based recommendation methods in top-k recommendations significantly and consistently.