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

机译：动态注意力集成神经网络用于基于会话的新闻推荐

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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.
机译：在线新闻推荐旨在不断选择符合用户偏好时间动态的候选文章库。现有的大多数方法都假定所有用户-项目交互历史对于推荐都是同等重要的，因为用户-项目交互有时充满随机性和偶然性，所以在推荐的情况下，这始终不适用。此外，以前基于会话的算法的工作仅考虑了当前会话中的用户序列行为，而没有考虑用户的历史兴趣或在此类会话中指出用户的主要目的。在本文中，我们提出了一种新颖的神经网络框架，即动态注意力集成神经网络，来解决这些问题。具体来说，我们提出了一种动态神经网络，可以在统一的框架中为用户个性化的新闻推荐建模用户的动态兴趣。新闻文章的语义嵌入，用户兴趣建模，基于会话的公共行为挖掘以及用于了解用户的关注度和会话内项目交互的注意力方案是在线序列挖掘和推荐策略的四个关键因素。在三个实际数据集上的实验结果表明，在基于会话的神经网络上的多个基准和最新方法方面，存在重大改进。

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