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

机译：在线新闻行业的情感推荐系统：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：在过去的十年中，推荐系统已经无处不在，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。人类的情感被广泛认为是行为和偏好的重要预测因子。它们是决策中的关键因素，但是直到最近，关于使用人的情感来个性化现实世界推荐系统的有效性的了解还很少。在本文中，我们介绍了情绪感知推荐系统（EARS），这是一种使用用户的自我评估的情绪反应来推荐新闻的大型系统。我们的原始贡献包括为新闻项推荐建立多维情感模型，引入可用于描述推荐项的情感项特征，构建情感相似性度量以及在大型真实语料库上验证EARS世界的网络流量。我们从两个新闻站点的2,700,000个唯一用户那里收集了13,000,000次页面浏览，并且对85,000条新闻收集了160,000多种情感反应。我们发现，将愉快的情绪纳入协作过滤建议中的性能始终优于所有其他算法。我们还发现，通过选择的情绪反应来提出针对性建议，为进一步研究提供了有希望的方向。作为一项额外的贡献，我们分享了我们在设计和开发基于情感的真实推荐引擎方面的经验，指出了部署基于情感的推荐者的实际方面所带来的各种挑战。

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