Do Social Explanations Work? Studying and Modeling the Effects of Social Explanations in Recommender Systems

Abstract

Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking out the recommended artist, and the actual rating of the artist based on listening to several songs. We find that while the explanations do have some influence on the likelihood, there is little correlation between the likelihood and actual (listening) rating for the same artist. Based on these insights, we present a generative probabilistic model that explains the interplay between explanations and background information on music preferences, and how that leads to a final likelihood rating for an artist. Acknowledging the impact of explanations, we discuss a general recommendation framework that models external informational elements in the recommendation interface, in addition to inherent preferences of users.

11 pages, WWW 2013

Cited by in corpus (4)

• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• A systematic review and taxonomy of explanations in decision support and recommender systems
• Towards Explainable Conversational Recommender Systems
• WeBrowse: Mining HTTP logs online for network-based content recommendation