Controversial Users Demand Local Trust Metrics: An Experimental Study on Epinions.com Community

By Paolo Massa, Paolo Avesani. AAAI Conference on Artificial Intelligence, 2005

398 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/00e85aa90893b7ec09d04eac72f9122620e82e8e

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

A survey of collaborative filtering based social recommender systems (2014, methodology): “Various techniques have been proposed to incorporate trust into CF approaches [46, 47, 50, 51, 52, 53, 54, 55, 56, 57].”
A survey of trust in computer science and the Semantic Web (2007, background): “In (Massa and Avesani, 2005), the problem of controversial users (those who are both trusted and distrusted) is addressed.”
Deep Network Embedding for Graph Representation Learning in Signed Networks (2019, background): “dataset [49] is a “who trust whom” online social network generated from the Epinions site, where one user can “trust” (positive) or “distrust” (negative) another.”
Predicting positive and negative links in signed social networks by transfer learning (2013, background): “Examples include Epinions(1) whose users can express trust or distrust of others [18], Slashdot(2) whose participants can declare others to be either “friends” or “foes” [11], and Wikipedia(3) whose users can vote for or against the promotion of others to administrator status [2].”
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases (2013, methodology): “It uses data collected from a previous study [23] together with additional statistics extracted from the website.”
Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. (2017, unknown): “Essentially, the results are personalized for each user [25].”
Predicting positive and negative links in online social networks (2010, background): “For example, users on Wikipedia can vote for or against the nomination of others to adminship [3]; users on Epinions can express trust or distrust of others [8, 18]; and participants on Slashdot can declare others to be either “friends” or “foes” [2, 13, 14].”
Schism (2010, background): “com, 2 partitions, a dataset derived from the eponymous social website [15].”
Exploiting homophily effect for trust prediction (2013, background): “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].”
A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “Researchers are increasingly noting that not all occurring connections reﬂect positive relationships [185]–[187].”
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization (2010, background): “Epinions is a signed user–user opinion network [21].”