Trust-Aware Collaborative Filtering for Recommender Systems

By Paolo Massa, Paolo Avesani. CoopIS/DOA/ODBASE, 2004

664 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/2512182cf3c4d7b3df549456fbeceee0a77c3954

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

In Sociology and Political Science

Citations in the corpus

FilmTrust: movie recommendations using trust in web-based social networks (2006, background): “Other work has touched on trust in recommender systems, including [11] and [12].”
Recommender Systems (2016, positive): “The effectiveness of such systems in the presence of cold-start is shown in [403, 404].”
Recommender systems with social regularization (2011, methodology): “In [24], a trust-aware method for recom-”
SoRec: social recommendation using probabilistic matrix factorization (2008, methodology): “In [14], a trust-aware collaborative filtering method for recommender systems is proposed.”
Recommender systems survey (2013, background): “social network [59,150,200] The ‘reputation’ of items is calculated studying how users work with”
Collaborative Filtering Recommender Systems (2011, methodology): “Massa and Avesani proposed a method for using users’ statements of their trust in other users’ opinions to weight user ratings by estimated trust rather than similarity in user– user CF when producing predictions and recommendations and built a ski mountaineering site around it [12, 91].”
Generating Predictive Movie Recommendations from Trust in Social Networks (2006, background): “Other work has touched on trust in recommender systems, including [9] and [10].”
Trust in recommender systems (2005, methodology): “Work in [11] also defines a trade-off situation between recommendation coverage and accuracy in the system.”
Introduction to Recommender Systems Handbook (2011, background): “For example, [34, 64] report that overall, social-networkbased recommendations are no more accurate than those derived from traditional CF approaches, except in special cases, such as when user ratings of a specific item are highly varied (i.”
Data Mining for Web Personalization (2007, background): “Massa and Avesani suggest another variant of MAE called the mean absolute user error that calculates the mean absolute error for each user and then averages over all users [74].”
Controversial Users Demand Local Trust Metrics: An Experimental Study on Epinions.com Community (2005, methodology): “Trust Metrics (Golbeck, Hendler, & Parsia 2003; Levien 2003; Ziegler & Lausen 2004; Massa & Avesani 2004) are a technique used to predict trust scores of users.”
Trust Metrics on Controversial Users: Balancing Between Tyranny of the Majority (2007, background): “A considerable amount of research has been carried on recently on these and related topics, such as trust metrics [7] [21] [11] [13] [9], reputation systems [19], and personalizing PageRank [10].”
Learning to recommend with social trust ensemble (2009, methodology): “Based on this intuition, many researchers have recently started to analyze trust-based recommender systems [1, 2, 13, 14, 15].”
Investigating interactions of trust and interest similarity (2007, methodology): “Epinions' rating and trust data have also been used by Massa and Avesani [38].”