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Affective recommender systems in online news industry: how emotions influence reading choices

机译：在线新闻行业中的情感推荐系统：情绪如何影响阅读选择

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Recommender systems have become ubiquitous over the last decade, providing users with personalized search results, video streams, news excerpts, and purchasing hints. Human emotions are widely regarded as important predictors of behavior and preference. They are a crucial factor in decision making, but until recently, relatively little has been known about the effectiveness of using human emotions in personalizing real-world recommender systems. In this paper we introduce the Emotion Aware Recommender System (EARS), a large scale system for recommending news items using user's self-assessed emotional reactions. Our original contribution includes the formulation of a multi-dimensional model of emotions for news item recommendations, introduction of affective item features that can be used to describe recommended items, construction of affective similarity measures, and validation of the EARS on a large corpus of real-world Web traffic. We collect over 13,000,000 page views from 2,700,000 unique users of two news sites and we gather over 160,000 emotional reactions to 85,000 news articles. We discover that incorporating pleasant emotions into collaborative filtering recommendations consistently outperforms all other algorithms. We also find that targeting recommendations by selected emotional reactions presents a promising direction for further research. As an additional contribution we share our experiences in designing and developing a real-world emotion-based recommendation engine, pointing to various challenges posed by the practical aspects of deploying emotion-based recommenders.
机译：在过去十年中，推荐系统已成为普遍存在的系统，为用户提供个性化的搜索结果，视频流，新闻摘录和购买提示。人类的情绪被广泛认为是行为和偏好的重要预测因子。它们是决策的关键因素，但直到最近，对使用人类情绪在个性化的现实世界推荐系统中的有效性相对较少。在本文中，我们介绍了使用用户的自我评估的情绪反应推荐新闻项目的情感意识推荐系统（耳朵），这是一个大规模的系统。我们的原始贡献包括制定新闻项目建议的多维情绪模型，引入情感项目功能，可用于描述建议的项目，情感相似度措施的构建，以及耳朵对真实的大语料库的验证-world web流量。我们从两个新闻网站的2,700,000个独特的用户收集超过13,000,000页面浏览量，我们收集超过160,000个的情绪反应到85,000个新闻文章。我们发现将令人愉快的情绪纳入协作过滤建议，始终如一地优于所有其他算法。我们还发现，选择的情绪反应的目标建议呈现进一步研究的有希望的方向。作为一项额外贡献，我们分享了我们在设计和开发基于世界情感的推荐引擎的经验，指出了部署基于情感推荐的实际方面所带来的各种挑战。

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