基于多维度兴趣注意力和用户长短期偏好的新闻推荐

Multi-Dimensional Interest-Attention-based News Recommendation with Long and Short-term User Preferences

在这个网络媒体平台成为获取新闻资讯的主流方式的时代,新闻推荐扮演着至关重要的角色。一方面,媒体平台使用新闻推荐可帮助用户过滤掉不感兴趣的新闻,定制个性化阅读内容推送;另一方面,智能推送服务能够增加新闻点击率,帮助媒体平台实现新闻的快速传播。目前,新闻推荐逐渐成为数据分发领域核心技术之一,逐渐引起国内外学者的关注。该文针对新闻热度不均衡问题造成的长尾现象,提出了一种基于多维度兴趣注意力的用户长短期偏好的新闻推荐模型。首先,对用户长期偏好进行挖掘时把用户兴趣分成多个维度,并采用注意力机制控制不同兴趣维度的重要程度,从而学习到包含不同维度兴趣信息的长期偏好。其次,采用CNN与注意力网络相结合的模型对新闻进行表示学习,采用GRU在用户近段时间内的阅读历史中学习用户短期偏好。最后,通过大量在真实新闻数据集上的实验,以AUC、MRR、NDCG为评价指标与其他基线方法进行比较,证实了该模型均优于其他方法。

At present,news recommendation has gradually become one of the core techniques and attracts many attention from various fields at home and abroad.Focusing on the unbalanced data issue,this paper proposes a news recommendation model with long and short-term user preference based on users’multi-dimensional interest.We divide the user long-term interests into several dimensions,and utilize the attention mechanism to distinguish the importance of different dimensions.In addition,we combine CNN and attention network to learn the news representations,and uses GRU to capture users’short-term preferences from their recent reading history..Experiments on a real-life news datasets show that our proposed model outperforms the state-of-the-art news recommendation methods in terms of AUC,MRR and NDCG.