Trust-Aware Recommender Systems

By Xin Liu, Anwitaman Datta, Ee-Peng Lim. 2014

617 citations recorded by the source corpus.

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

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

How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “Alternatively, the user preferences (t) can represent the non-binary trust between users, which can be provided explicitly by the user [44] or learned from user behavior [13]; we use the latter in our analysis (section 5).”
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].”
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, background): “It contains 13,668,320 observed comments from 120,492 users on 775,760 articles, which is collected by Trustlet website [42].”
Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation (2014, background): “On one hand, some users may not provide sufficient records, in which case it is crucial to predict user preference dynamically according to user feedback [16, 17].”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “…is substantially different from both the Tucker model and the CP model in that it involves low-order interactions explicitly (i.e., pairwise interactions between have proposed low-order tensor decomposition that combines multiple-order TF models to decompose [user, item, tag, usage (boolean)] data.”
Recommender Systems (2016, positive): “The effectiveness of such systems in the presence of cold-start is shown in [403, 404].”
Make it personal: A social explanation system applied to group recommendations (2017, positive): “In the literature, e.g. (Golbeck, 2006; Jamali and Ester, 2009; Massa and Avesani, 2007) it was shown that using Social Network information in addition to feedback data (e.g. ratings) can significantly improve group recommendations’ accuracy.”
Controlling Popularity Bias in Learning-to-Rank Recommendation (2017, background): “…made by 6,040 MovieLens users who joined MovieLens in 2000 [8]. second dataset is the Epinions dataset, which is gathered from a consumers opinion site where users can review items (such as cars, books, movies, and software) and assign them numeric ratings in the range 1 (min) to 5 (max) [11].”
Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, background): “Thus, in order to consider the ratings of users that are not directly connected, various approaches propagate the trust among their users [46, 16, 33, 35].”
The MovieLens Datasets: History and Context (2016, background): “…2001; Deshpande and Karypis 2004], dimensionality reduction collaborative .ltering [Sarwar et al. 2000], trust-based recommenders [O Donovan and Smyth 2005; Massa and Avesani 2007], recommenders on rapidly changing sets of items [Das et al. 2007], and cold-start algorithms [Schein et al. 2002].”
Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. (2017, neutral): “Explicit trust denotes the trust values explicitly indicated by their users [30] [9] [16].”
Inferring Latent Network from Cascade Data for Dynamic Social Recommendation (2016, background): “Early research [11] [21] searches the trust network to determine the recommended items.”
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).”
Social contextual recommendation (2012, methodology): “Trust-based approaches [22, 23, 11] exploit the trust network among users and make recommendations based on the ratings of users who are directly or indirectly trusted.”
Shilling attacks against recommender systems: a comprehensive survey (2012, background): “Trust-aware recommender systems are more robust prediction schemes than traditional ones against shilling attacks (Massa and Avesani 2007; Ray and Mahanti 2010).”
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation (2020, background): “Epinions is a social based product review platform and the dataset is introduced in [30] and is publicly available.”
Ieee Transactions on Knowledge and Data Engineering, Manuscropt Id 1 Social Recommendation with Cross-domain Transferable Knowledge (neutral): “Tie strength can refer to homophily [5], circle-based influence [6][7][8], or social trust [9][10].”