计算机科学
推荐系统
协同过滤
人工智能
期限(时间)
匹配(统计)
偏爱
用户建模
矩阵分解
特征(语言学)
深度学习
机器学习
偏好学习
数据挖掘
情报检索
用户界面
操作系统
统计
物理
哲学
特征向量
经济
量子力学
微观经济学
语言学
数学
作者
Ruiqin Wang,Zongda Wu,Jungang Lou,Yunliang Jiang
标识
DOI:10.1016/j.eswa.2021.116036
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.
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