Generating Predictive Movie Recommendations from Trust in Social Networks

By Jennifer Golbeck. Trust Management, 2006

468 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/d9b3daaa91e031d3c11dee6e8cb643b0fa00547a

How researchers see this paper

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

In Computer Networks and Communications

In Information Systems

Citations in the corpus

Introduction to Recommender Systems Handbook (2011, background): “This observation, combined with the growing popularity of open social networks, is generating a rising interest in community-based systems or, as or as they usually referred to, social recommender systems [34].”
A model of a trust-based recommendation system on a social network (2006, methodology): “In the following, we present a model of a trust-based recommendation system which, in an automated and distributed fashion, filters information for agents based on the agents’ social network and trust relationships [6,15,19,25].”
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems (2019, methodology): “To this end, social recommendation, which attempts to leverage the social networks of users to incorporate more useful information, has been proposed to alleviate these two problems [5, 12, 18, 19].”
Make it personal: A social explanation system applied to group recommendations (2017, unknown): “In the literature, e.g. (Golbeck, 2006; Jamali and Ester, 2009; Massa and Avesani, 2007) it was shown that using Social Network information in addition to feedback data (e.g. ratings) can significantly improve group recommendations’ accuracy.”
Exploiting homophily effect for trust prediction (2013, methodology): “RELATED WORK Trust plays an important role in helping online users collect reliable information and trust prediction, inferring unknown trust relations among pairs of users, attracts more and more attention in recent years [7, 5, 14, 10, 17, 13, 1].”
Recommender Systems (2016, unknown): “These methods have a particularly beneficial effect on recommendations to controversial users whose ratings on items differ significantly from other users [223].”
Collaborative Filtering Recommender Systems (2011, methodology): “Golbeck [47] used a similar integration method, based on a different trust estimation algorithm, for movie recommendation.”
Recommender Systems (2012, background): “Trust can be used instead of user similarity [239], in combination with collaborative filtering to help deal with data sparsity and the cold start problem [240, 241, 242], or it can help to further filter recommendations by prioritizing those approved by trusted sources (see [243] for a review).”
Social media recommendation based on people and tags (2010, background): “Several studies suggest incorporating explicit social network information in CF systems to improve the quality of recommendation in domains such as movies and books (e.g., [3,12,30]), music [20], clubs [14], and news stories [21].”
Protecting privacy when mining and sharing user data (2012, background): “Golbeck [65] uses the trust relationships expressed through social connections for personalized movie recommendations.”
Use of social network information to enhance collaborative filtering performance (2010, methodology): “Social networks have been utilized for making recommendations in diverse application domains such as movies (Golbeck, 2006), Web pages (Kautz et al.”
Inspectability and control in social recommenders (2012, background): “In social recommenders, the degree of trustfulness (or “trusting propensity”) could play an important role, because in order to accept the recommendations, users will need to trust their friends’ preferences [14, 36].”
Recommender Systems: Introduction and Challenges (2015, background): “the RSs that have been developed to be dedicated to social media, recommender systems of other domains can benefit from the new types of data that social media introduces about users to enhance the quality of standard RSs [23].”