The Limits of Popularity-Based Recommendations, and the Role of Social Ties

By M. Bressan, S. Leucci, Alessandro Panconesi, Prabhakar Raghavan, E. Terolli. 2016

24 citations recorded by the source corpus.

In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users. We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form. Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others. Our theoretical results are complemented by experiments with real world social networks showing that social graphs prevent large market distortions in spite of the presence of highly influential users.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiang Wang
2. Xiangnan He
3. Fuli Feng
4. Hongwei Wang
5. Meng Wang
6. Chen Gao
7. Le Wu
8. Jiawei Chen

Citations in the corpus

• Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, background): “To mitigate this issue, many approaches have been proposed to utilize social network information in recommender systems [3, 6, 10, 12, 25, 29, 31, 32].”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Popularity Bias Regularization [106], [107], [108], [109] Adversarial learning [110] Causal graph [32] Others [111], [112]”