Exploring social influence for recommendation: a generative model approach

By Mao Ye, Xingjie Liu, Wang-Chien Lee. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012

321 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9285f3c70c6cab770d2f7d4ccb32ecae0397b134

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In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

The Limits of Popularity-Based Recommendations, and the Role of Social Ties (2016, background): “An emblematic example of RS based on social ties is [31], which uses a generative influence model where users probabilistically pick items according to both their and their friends’ preferences.”
Time-aware point-of-interest recommendation (2013, background): “There exists other work that incorporates social link information into POI recommendations, such as the probabilistic generative model-based method [21], and matrix factorization-based method [4].”
Influence Maximization on Social Graphs: A Survey (2018, background): “Besides viral marketing, IM is also the cornerstone in many other important applications such as network monitoring [3], rumor control [4], [5], and social recommendation [6].”
Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering (2014, unknown): “Furthermore, In [23], Ye et al.”
Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, background): “[52] propose a generative model to describe users’ behavior, given influences from social communities.”
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015, background): “Refs [24, 35] incorporate this idea more directly by including friends’ latent representations in computing recommendations made for a user.”
Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “As suggested in (Ye, Liu, and Lee 2012), the effectiveness of recommender systems with sparse datasets is usually very low.”
Exploring temporal effects for location recommendation on location-based social networks (2013, background): “Specifically, the authors investigated the geographical influence [25] and social influence [23] for location recommendation, and discovered that user preference plays a more important role in contributing to the recommendation than social and geographical influence.”