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

arXiv: https://arxiv.org/abs/1304.3405 · HTML: https://mlsanity.com/paper/1304.3405/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2013.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Unlocking the Potential of Large Language Models for Explainable Recommendations](https://mlsanity.com/paper/2312.15661/) (2023)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Graph-based Extractive Explainer for Recommendations](https://mlsanity.com/paper/2202.09730/) (2022)
- [Towards Explainable Conversational Recommender Systems](https://mlsanity.com/paper/2305.18363/) (2023)
- [Counterfactually Evaluating Explanations in Recommender Systems](https://mlsanity.com/paper/2203.01310/) (2022)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [Learning to Counterfactually Explain Recommendations](https://mlsanity.com/paper/2211.09752/) (2022)
- [Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems](https://mlsanity.com/paper/2510.07621/) (2025)
- [Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach](https://mlsanity.com/paper/2410.04552/) (2024)

## Abstract (arXiv)

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.