A matrix factorization technique with trust propagation for recommendation in social networks

By Mohsen Jamali, Martin Ester. 2010

1,719 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

How researchers see this paper

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

In Artificial Intelligence

In Computer Networks and Communications

In Information Systems

Citations in the corpus

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018, background): “To address these limitations, researchers have proposed incorporating side information into CF, such as social networks [9], user/item attributes [32], images [43] and contexts [26].”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “ularity will perform better on this data. Facebook and Etsy are among websites that use social networks to recommend content. Many algorithms use social network information to improve recommendations [13,22,26,41,42,65] but when training and evaluating these algorithms on data from social platforms,”
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017, methodology): “For Flixster, the user and item graphs were constructed from the scores of the original matrix.”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “…of users has been incorporated into MF for improving the learning of latent user factors and, as a result, improving the recommendation performance [Jamali and Ester 2010; Ma et al. 2009]. information. fLDA makes use of latent Dirichlet allocation (LDA) [Blei et al. 2003] to regularize a…”
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (2019, background): “nnnnnnn side information in recommender systems, including social networks [10], attributes [30], and multimedia (e.”
Multi-relational matrix factorization using bayesian personalized ranking for social network data (2012, background): “Jamali and Ester [3] extend the work of Ma et al.”
UniWalk: Explainable and Accurate Recommendation for Rating and Network Data (2017, methodology): “It is crawled by Jamali and Etser [10].”
Dual-Primal Graph Convolutional Networks (2018, methodology): “We repeated verbatim the experiment of [34, 27] on several standard datasets used in the recommender systems literature (MovieLens [32], Flixster [23], Douban [30], and YahooMusic [11]), using different convolutional layers inside RMGCNN (Chebyshev [10], Cayley [27], GAT [42] and the proposed…”
Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, methodology): “CliMF Maximize reciprocal rank method for item ranking [37] LRMF List-wise learning to rank method for item ranking [36] NMF Non-negative matrix factorization [20] PMF Probabilistic matrix factorization [28] SoMF Trust propagation matrix factorization [15] TrMF Trust social matrix factorization [50]”
Graph Neural Networks for Social Recommendation (2019, background): “• SocialMF [13]: It considers the trust information and propagation of trust information into the matrix factorization model for recommender systems.”
LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data (2014, background): “Besides them, there are also several other approaches considering non-linear characteristics of nodes and links, such as exponential family models [14] and probabilistic matrix factorization models [32], [33].”
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, methodology): “Since on Goodreads and Flixster a friend’s rating is shown in a user’s feed irrespective of whether it was high or low, we consider all ratings by users for our analysis9.”
A Data-Based Approach to Social Influence Maximization (2011, methodology): “com) is one of the main players in the mobile and social movie rating business [9].”
Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019, background): “Modeling the influence of friends on user interests has also received attention [15, 16, 23–25].”
BlurMe: inferring and obfuscating user gender based on ratings (2012, background): “[5] has 1M users, of which only 34.”
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, background): “It includes 8,196,077 known entries from 147,612 on 48,794 movies, which is collected from the Flixter commercial website [43]. d) D4: Douban.”