Trust in recommender systems

By John O'Donovan, Barry Smyth. International Conference on Intelligent User Interfaces, 2005

978 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5e622aca9c687598dc2e1f00de19a887ecf706db

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 Management Science and Operations Research

Citations in the corpus

TrustWalker: a random walk model for combining trust-based and item-based recommendation (2009, methodology): “Trust has been deflned and used in a difierent way by [ 12 ].”
Recommender Systems (2016, neutral): “The work in [187] showed how to infer trust relationships directly from ratings data [187], although there is some debate on whether this notion of trust is generally accepted as it is conventionally used in the literature.”
Personalized Recommendation Combining User Interest and Social Circle (2014, neutral): “Many social network based models [3], [13]–[19], [33]– [37] have been proposed to improve the performance of the RS. Recently, Yang et al. [1] propose to use the concept of 'inferred trust circle' based on the domain-obvious circles of friends on social networks to recommend user favorite items.”
A survey of trust in computer science and the Semantic Web (2007, background): “…[39] (Yu and Singh, 2002) [40] (Yu and Singh, 2003) [41] (Sabater and Sierra, 2002) [42] (Beth et al., 1994) [43] (Xiao and Benbasat, 2003) [44] (O'Donovan and Smyth, 2005) [45] Trust metrics in a web of trust (Goldbeck and Hendler, 2004a) [51] (Goldbeck and Hendler, 2004b) [52] (Stewart, 1999)…”
Why I like it: multi-task learning for recommendation and explanation (2018, background): “In order to build trust between a recommender system and its users, it has become important to complement recommendations with explanations so that users can understand why a particular item has been suggested [36, 49].”
Recommendation systems: Principles, methods and evaluation (2015, background): “Following the studies, it can be deduced that computational trust models can act as appropriate means to supplement or completely replace current collaborative filtering technique [73].”
Circle-based recommendation in online social networks (2012, background): “Several social-trust based RSes have recently been proposed to improve recommendation accuracy, to just name a few, [9], [10], [11], [12], [13], [15], [17], [18], and [19].”
A review of ontology based query expansion (2007, background): “Another interesting dimension in Recommender Systems is that of trust (O'Donovan & Smyth, 2005).”
Recommender systems survey (2013, background): “Trust and reputation is an important area of research in RS [166]; this area is closely related to the social information currently included in RS [114].”
Trust building with explanation interfaces (2006, background): “They also showed that providing explanations can improve the acceptance of ACF systems and potentially improve users filtering performance.”
Recommender system application developments: A survey (2015, background): “O'Donovan [61] claimed that user similarity may be overemphasized.”
Recommender systems with social regularization (2011, methodology): “In recent work proposed in [23], Ma et al. developed a factor analysis method based on the probabilistic graphical model, which fuses the user-item matrix with the users’ social trust networks by sharing a common latent low-dimensional user feature matrix.”