Using Trust in Recommender Systems: An Experimental Analysis

By Paolo Massa, Bobby Bhattacharjee. Trust Management, 2004

355 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/956641456991b022947324df276a0108a2d174aa

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Citations in the corpus

E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact (2007, methodology): “…most common typology of RAs is based on filtering methods: (1) content-filtering RAs, and (2) collaborative-filtering RAs (Adomavicius and Tuzhilin 2003; Ansari et al. 2000; Cosley et al. 2003; Massa and Bhattacharjee 2004; Schein et al. 2002; Wang and Benbasat 2004a; West et al. 1999; Zhang 2002).”
Facing the cold start problem in recommender systems (2014, background): “In Massa and Bhattacharjee (2004), the authors try to assert the cold start problem through a trust-aware system that takes into account the ‘web of trust’ provided by every user.”
Trust in recommender systems (2005, methodology): “[12] argue that this trust data can be extracted and used as part of the recommendation process, especially as a means to relieve the sparsity problem that has hampered traditional collaborative filtering techniques.”
Trust-Aware Collaborative Filtering for Recommender Systems (2004, background): “In [9] we have shown how this predicted trust value, thanks to trust propagation, is computable on much more users than the user similarity value.”
Recommender Systems (2016, neutral): “The work in [405] discusses methods for weighting the different components of the aggregation based on path length or based on closeness of friendships.”
Evaluating Recommendation Systems (2011, background): “Some literature on recommender systems uses such trust measurements to filter similar users [39] and Chapter 20.”
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).”
On the stability of recommendation algorithms (2010, background): “In particular, since recommender systems depend heavily on input from users, they are subject to manipulations and attacks [13-17].”
Investigating interactions of trust and interest similarity (2007, background): “[64], Epinions is dealt with in Ref. [ 39 ]. 4.2. Similarity computation”
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].”
Generating Predictive Movie Recommendations from Trust in Social Networks (2006, background): “Other work has touched on trust in recommender systems, including [9] and [10].”
A survey of collaborative filtering based social recommender systems (2014, background): “For instance, [30] attempts to address the rating sparsity issue using the trust relationship.”