Collaborative filtering for orkut communities: discovery of user latent behavior

By Wen-Yen Chen, Jon-Chyuan Chu, Junyi Luan, Hongjie Bai, Yi Wang, Edward Y. Chang. 2009

187 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

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In Statistical and Nonlinear Physics

Citations in the corpus

Recurrent Latent Variable Networks for Session-Based Recommendation (2017, background): “Ratings-based collaborative ltering (CF) systems have served as an eective approach to address this challenge [7].”
Strathprints Institutional Repository Bayesian Latent Variable Models for Collaborative Item Rating Prediction. In: Acm Cikm, 2011-03-31. Bayesian Latent Variable Models for Collaborative Item Rating Prediction (background): “In other recent work Chen et al. [5] compare the performance of Latent Dirichlet Allocation (LDA) [2] with association rule mining (ARM) for the purpose of community recommendation .”
Recommender Systems (2012, methodology): “LDA was applied to predict review scores based on the content of reviews [194] and to uncover implicit community structures in a social network [195].”
You are what you like! Information leakage through users' Interests (2012, background): “Computing the neighbors of each user in the feature space (i.e., whose IFVs are similar in the feature space) to discover similar users, and exploiting this neighborhood to infer hidden attributes.”
WarpLDA: a Simple and Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015, background): “Advantages of WarpLDA include 1) Efficiency and scalability: WarpLDA has good locality and carefully designed partition method, and can be scaled to hundreds of machines; 2) Simplicity: WarpLDA does not have any complicated modules such as alias tables, hybrid data structures, or parameter servers,…”
LCARS: a location-content-aware recommender system (2013, methodology): “To evaluate the recommender models, we adopt the testing methodology and the measurement Recall@k applied in [7, 5, 13, 32].”
Challenging the Long Tail Recommendation (2012, methodology): “[15, 5, 17] use LDA-based method for recommendation.”
Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization (2016, methodology): “Group recommendation The problem of recommending groups to users has been studied elsewhere (Chen et al. 2009; Chen, Zhang, and Chang 2008; Vasuki et al. 2010; Wang et al. 2012; Zeng and Chen 2013) through exploring a variety of probabilistic and combinatorial recommendation methods applied to Boole”
Nonparametric bayesian multitask collaborative filtering (2013, background): “approach to address the problem of discovering items of interest [4].”
Improving one-class collaborative filtering by incorporating rich user information (2010, methodology): “Here we use another recall-oriented metric called Mean Percentage Ranking, which is used in [11] and [4], to measure the user satisfaction of items in an ordered list.”
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015, background): “Our empirical results in a wide range of testing conditions demonstrate that WarpLDA is consistently 5-15x faster than the state-of-the-art MH-based LightLDA, and is faster than the state-of-the-art sparsity aware F+LDA in most settings.”