Computing and Applying Trust in Web-based Social Networks

By Jennifer Golbeck. 2005

974 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fadc0059a2ef31e19a8f98e2516d3e28ed9c5975

How researchers see this paper

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

Citations in the corpus

Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, background): “Does SR fare better for cold star users? The belief is that SR deals better with cold start users [20] (with less than 5 items rated [16]) as it only requires them to be connected to other users to make predictions.”
Trustworthiness Management in the Social Internet of Things (2014, background): “The main properties of trust are well deﬁned and many works contribute to describe them ([36], [37], [38], [39] and [40]).”
Recommender system application developments: A survey (2015, methodology): “A systematic algorithm, TidalTrust, was proposed by Golbeck [54] to address the trust-based rating prediction problem and is considered to be effective in the forming process of numeric trust networks in several systems.”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, methodology): “…random fold splits are not the same, using the same experimental setup, Huang et al. (2013) also scored the precision-recall area for negative trust of standard trust prediction algorithms EigenTrust (Kamvar et al., 2003) and TidalTrust (Golbeck, 2005), which scored 0.131 and 0.130, respectively.”
A generalized taxonomy of explanations styles for traditional and social recommender systems (2012, background): “…Tuzhilin 2008) X X X … … … … … … … … Multi-criteria (Adomavicius et al. 2010) X X X Social tagging (Vig et al. 2009; Marinho et al. 2010) X X X X X X X Trust aware (Golbeck 2005; Massa and Avesani 2007) X X X X X X X additional contextual information, such as time, location, weather, or the…”
Predicting personality with social media (2011, background): “In January 2005, a survey of social networking websites estimated that among all sites on the web there were roughly 115 million members [14].”
Generating Predictive Movie Recommendations from Trust in Social Networks (2006, background): “Details of that algorithm are beyond the scope of this paper, but can be found in [11] and [12].”
Recommender Systems (2016, unknown): “The doctoral dissertation of Jennifer Golbeck [222] provides several seminal algorithms on the topic.”
A matrix factorization technique with trust propagation for recommendation in social networks (2010, background): “TidalTrust[3] performs a modiﬁed breadth ﬁrst search in the trust network to compute a prediction.”
Trust-aware recommender systems (2007, methodology): “There are many diﬀerent trust metrics [2, 11, 6, 9].”
Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. (2017, unknown): “There are various ways to the different degrees of trust [14] [19].”