News Recommendation with Multi-views Emotion Analysis

Hang Yun, Xing Deng, Yulu Du
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引用次数: 1

Abstract

News recommendation aiming to find attractive news for users has been received many attentions in recent years. Existing news recommendation methods mainly focus on modeling user preference based on the interaction behaviors between users and news without the consideration of emotion information in the interaction. However, emotion information also plays an important role in improving the accuracy of news recommendation. In this paper, we propose an emotion analysis method for news recommendation with using multi-views to explore the impact of emotion information during the process of user's decision making. The emotion features extracted by the method are combined with the content features of the news to provide a comprehensive feature representation of the candidate news to improve the performance of recommendation. Experiments on real-world datasets show the effectiveness of the proposed method in improving accuracy of news recommendation.
基于多视角情感分析的新闻推荐
近年来,以为用户发现有吸引力的新闻为目的的新闻推荐备受关注。现有的新闻推荐方法主要是基于用户与新闻的交互行为对用户偏好进行建模,而没有考虑交互中的情感信息。然而,情感信息对于提高新闻推荐的准确性也起着重要的作用。本文提出了一种面向新闻推荐的情感分析方法,利用多视角来探讨情感信息在用户决策过程中的影响。将该方法提取的情感特征与新闻的内容特征相结合,为候选新闻提供全面的特征表示,以提高推荐的性能。在真实数据集上的实验表明,该方法可以有效地提高新闻推荐的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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