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

Abstract

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.

Trends in combating fake news on social media – a survey

“…The recommendation system approach tries to verify some news content deem to be authentic and then recommend these news articles for consumption (Özgöbek et al, 2019). Mizgajski and Morzy (2019) examine recommendation systems in the online news industry and posits that most recommendation system uses human emotions to recommend content. The collaborative filtering recommendation method (Garcin et al, 2012) recommends news content based on ratings and comments from others.…”

Trends in content-based recommendation

“…The work Affective recommender systems in online news industry: how emotions influence reading choices (Mizgajski and Morzy 2018) studies the role of emotions in the recommendation process. Based on a set of affective item features, a multi-dimensional model of emotions for news item recommendation is proposed.…”

Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content

“…Research into context-aware recommendation might help to better capture such differences. As of now, context-aware news recommendation is largely limited to location, time of day, or device used (Asikin and Wörndl, 2014;De Pessemier et al, 2016;Lommatzsch et al, 2017), but there have also been efforts to capture more complex constructs such as emotions (Mizgajski and Morzy, 2019). Further work into this direction could help better capture users' situational information needs.…”