Application of Dimensionality Reduction in Recommender System - A Case Study

By Badrul Munir Sarwar, George Karypis, J. Konstan, John Riedl. 2000

1,572 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/c3eddc356634d8c0d6db385567deffe1cf7cb3f6

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In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Tagsplanations: explaining recommendations using tags (2009, methodology): “SVD-based approaches, which have proven effective for predicting users’ ratings of items [9], might also might be utilized to estimate users’ preferences for tags.”
Understanding and improving automated collaborative filtering systems (2000, methodology): “Since then, the GroupLens Research Project at the University of Minnesota has conducted further research in ACF systems, covering areas such as matrix storage methods for ACF [31], integration of content-based filterbots to 16 improve prediction quality [30,78], combining independent filtering agent”
Recommender Systems (2016, positive): “The use of dimensionality reduction methods for neighborhood-based filtering was proposed in [525].”
Data Mining for Web Personalization (2007, methodology): “Two commonly used metrics in this context are Precision and Recall which are standard metrics used in evaluating information retrieval effectiveness, but have also been adopted to evaluate ranked ordering of recommended items in personalization [58, 116, 15].”
Learning Image and User Features for Recommendation in Social Networks (2015, methodology): “To alleviate the sparsity problem, matrix factorization based CF models have been proposed, such as the Singular Value Decomposition (SVD) [25], Weighted Matrix Factorization (WMF) [13], and the combination of probabilistic matrix factorization (PMF) [19] and topic models [31].”
An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems (2014, background): “Further, with a properly designed loss function, the prediction accuracy of MF-based models is very competitive [1]–[3], [9]–[19].”
A generalized maximum entropy approach to bregman co-clustering and matrix approximation (2004, methodology): “14 shows the mean absolute error (MAE) obtained using various existing collaborative filtering approaches (Sarwar et al., 2000; Hofmann, 2004; Resnick et al., 1994) as well as the co-clustering approach based on squared Euclidean distance.”
A probabilistic model to resolve diversity–accuracy challenge of recommendation systems (2015, background): “It is well known that in order to have an acceptable performance for a recommender system, the constructed connectivity matrices should be relatively dense [27].”
Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System (2015, methodology): “2 Dimensionality reduction techniques To reduce the problems from high levels of sparsity in RS databases, certain studies have used dimensionality reduction techniques [6].”
Collaborative filtering recommender systems (2013, methodology): “…model of model-based filtering in the past (Sarwar et al ., 2001), machine learning algorithms such as Bayesian networks (Breese et al ., 1998), clustering (Basu et al ., 1998; Breese et al ., 1998; Ungar and Foster, 1998) and rule-based approaches (Sarwar et al ., 2000a) have been commonly used.”
Item-based collaborative filtering recommendation algorithms (2001, methodology): “To explore that we took an algorithmic approach and used Latent Semantic Indexing (LSI) to capture the similarity between users and items in a reduced dimensional space [24, 25].”
Unifying user-based and item-based collaborative filtering approaches by similarity fusion (2006, background): “Lately, researchers have introduced dimensionality reduction techniques to address data sparsity [4, 13, 16].”
A new user similarity model to improve the accuracy of collaborative filtering (2014, methodology): “Beyond that, dimensionality reduction technique, such as principle component analysis (PCA) [24] and singular value decomposition (SVD) [25], is commonly used to alleviate the problem.”
Discriminatively Activated Sparselets (2013, background): “…2013 by the author(s). under a variety of guises, such as steerable filter banks (Freeman & Adelson, 1991), low-rank approximations for collaborative filtering (Sarwar et al., 2000), and shared part models for object detection (Zhu et al., 2010; Ott & Everingham, 2011; Girshick et al., 2011).”
Mining user similarity based on location history (2008, methodology): “It is an intuitive method that most people might conceive of. The rest are cosine similarity and Pearson similarity measures which have been widely used in recommendation systems, and have been claimed in paper [ 18 ] to outperform other existing similarity measures.”