A Probabilistic Model for Using Social Networks in Personalized Item Recommendation

By Allison June-Barlow Chaney, David M. Blei, Tina Eliassi-Rad. 2015

182 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Transportation

How researchers see this paper

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

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Xu Chen
4. Tat-seng Chua
5. Yongfeng Zhang
6. Fuli Feng
7. Meng Wang
8. Guodong Long

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Xu Chen
4. Fuli Feng
5. Meng Wang
6. Yongfeng Zhang
7. Tat-seng Chua
8. Jiawei Chen

In Transportation

1. Barbara E. Engelhardt
2. Brandon M Stewart
3. Chengqi Zhang
4. Yongfeng Zhang
5. Guodong Long
6. Xiang Wang
7. Fuli Feng
8. Allison June-Barlow Chaney

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “Chaney et al. [2015] presented social Poisson factorization, a Bayesian model that incorporates a user’s latent preferences for items with the latent influences of her friends, which provides a source of explainable serendipity (i.”
• How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “Full collaborative ltering system can be supplemented with content information [61] or augmented with social ltering [13].”
• Inferring Latent Network from Cascade Data for Dynamic Social Recommendation (2016, background): “Moreover, social influence is also considered in social recommendation, such as conditional random field [30] and probabilistic Poisson factorization [2].”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Moreover, conformity bias might be caused by social influence, where users tend to behave similarly with their friends [25], [26], [27], [28].”
• UniWalk: Explainable and Accurate Recommendation for Rating and Network Data (2017, background): “Chaney et al. [3] develop a probabilistic model that integrates social network with traditional matrix factorization method.”
• The Limits of Popularity-Based Recommendations, and the Role of Social Ties (2016, background): “They are recognized to be an important factor that shape user choices, and recommenders try to leverage them in a variety of ways [22, 31, 33, 6].”
• Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019, unknown): “Others studied merely social influences [4, 24, 41].”