Dynamic attention-integrated neural network for session-based news recommendation

被引:35
作者
Zhang, Lemei [1 ]
Liu, Peng [1 ]
Gulla, Jon Atle [1 ]
机构
[1] Norwegian Univ Sci & Technol, Dept Comp Sci, Trondheim, Norway
关键词
Personalized news recommendation; Recurrent neural networks; User interest modelling; Attention model;
D O I
10.1007/s10994-018-05777-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Online news recommendation aims to continuously select a pool of candidate articles that meet the temporal dynamics of user preferences. Most of the existing methods assume that all user-item interaction history are equally importance for recommendation, which is not alway applied in real-word scenario since the user-item interactions are sometime full of stochasticity and contingency. In addition, previous work on session-based algorithms only considers user sequence behaviors within current session without incorporating users' historical interests or pointing out users' main purposes within such session. In this paper, we propose a novel neural network framework, dynamic attention-integrated neural network, to tackle the problems. Specifically, we propose a dynamic neural network to model users' dynamic interests over time in a unified framework for personalized news recommendations. News article semantic embedding, user interests modelling, session-based public behavior mining and an attention scheme that used to learn the attention score of user and item interaction within sessions are four key factors for online sequences mining and recommendation strategy. Experimental results on three real-world datasets show significant improvements over several baselines and state-of-the-art methods on session-based neural networks.
引用
收藏
页码:1851 / 1875
页数:25
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