Recommender systems with social regularization

By Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, Irwin King. 2011

1,713 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Recommender Systems

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Citations in the corpus

From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “The emergence of new products may cause users to change their focus [19]; older movies may be treated more favorably once they are considered ‘classics’ [20]; and users may be inﬂuenced by general trends in the community, or by members of their social networks [26].”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “Similarly, the Douban service contains popularity features; algorithms that include a notion of popularity should perform better on the publicly available data from this platform [42].”
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017, methodology): “For the Douban dataset, we used only the user graph (the provided social network of the user).”
Geometric Deep Learning: Going beyond Euclidean data (2016, background): “structure into matrix completion problems [94], [95], [96], [97] in the form of column- and row graphs representing similarity of users and items, respectively (see Figure 4).”
A Survey of Graph Neural Networks for Social Recommender Systems (2022, methodology): “In the early days, research focused on matrix factorization ( MF ) techniques [28, 54– 57, 86, 116].”
A Neural Influence Diffusion Model for Social Recommendation (2019, background): “endation With the prevalence of online social platforms, social recommendation has emerged as a promising direction that leverages the social network among users to enhance recommendation performance [7, 17, 24]. In fact, social scientists have long converged that as information diffuses in the soci”
Collaborative Filtering-Based Recommendation of Online Social Voting (2017, background): “poses unique challenges and opportunities for RSs utilizing social trust information [14], [26], [28], [32], [34].”
A Survey of Signed Network Mining in Social Media (2015, background): “Therefore, to capture positive links, Ma et al. [Ma et al. 2011] added a term to minimize the distance of the preference vectors of two users with a positive link based on Eq.”
Factorization Bandits for Interactive Recommendation (2017, methodology): “…solutions, where the dependency among users (such as social network) is introduced as graph-based regularization over the latent user factors (Ma et al. 2011; Cesa-Bianchi, Gentile, and Zappella 2013), we encode such dependency directly into our reward generation assumption for matrix…”
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (2017, methodology): “To evaluate the recommendation performance, we adopt the rootmean-square-error (RMSE) as our metric, which is the most popular one for rating prediction in the recommendation literature [14, 20, 22].”
Exploiting geographical influence for collaborative point-of-interest recommendation (2011, background): “The results indicate that the tastes of a user’s friends may actually vary significantly, which has also be discussed in [16] recently.”
Graph Neural Networks for Social Recommendation (2019, background): “The exploitation of social relations for recommender systems has attracted increasing attention in recent years [18, 28, 30].”
Circle-based recommendation in online social networks (2012, methodology): “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].”
TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings (2015, methodology): “2008), RSTE (Ma, King, and Lyu 2009), SoReg (Ma et al. 2011), SocialMF (Jamali and Ester 2010), TrustMF (Yang et al.”
Heterogeneous Information Network Embedding for Recommendation (2017, background): “[19] integrate social relations into matrix factorization in recommendation.”
MMALFM (2018, background): “For example, various types of side information have been incorporated into MF to alleviate the cold-start problem, such as tags [55], social relations [42, 53], reviews [41, 45, 63], visual features [28], and contextual information [15, 16].”
Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks (2012, methodology): “PMF with Social Regularization (PMFSR) : this method is proposed to include the social friendship under the PMF framework (Ma et al. 2011b) : this is the fused matrix factorization framework with the Multi-center Gaussian Model (FMFMGM).”