Effective missing data prediction for collaborative filtering

By Hao Ma, Irwin King, Michael R. Lyu. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

524 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/b56a78f559d47ee16b1d3ccf5cc5e4be29c1088b

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In Computer Vision and Pattern Recognition

In Information Systems

In Transportation

Citations in the corpus

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.”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, methodology): “The KNN and NN methods have two subtypes, user-based and item-based [95,164].”
Recommender systems with social regularization (2011, background): “Related recommendation techniques have been widely studied in research communities of information retrieval [5, 8, 12, 21, 36, 38, 39, 40], machine learning [28, 30, 31, 33, 41] and data mining [1, 3, 11, 14, 15].”
WSRec: A Collaborative Filtering Based Web Service Recommender System (2009, methodology): “Collaborative filtering methods are widely adopted in commercial recommender systems [9, 12].”
Learning to recommend with social trust ensemble (2009, methodology): “in [12] proposed two fusion models to combine user-based method with itembased method.”
QoS Ranking Prediction for Cloud Services (2013, background): “The most analyzed examples of memory-based collaborative filtering include user-based approaches [4], [9], item-based approaches [7], [11], [16], and their fusion [13], [16], [17], [22], [23].”
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation (2019, methodology): “From a more technical perspective, our proposed approach can also be seen as a label propagation method to address missing data problem in recommender systems [19, 21].”
UserRec: A User Recommendation Framework in Social Tagging Systems (2010, methodology): “The other memory-based approach we compare is the algorithm proposed by (Ma, King, and Lyu 2007).”
Probabilistic factor models for web site recommendation (2011, methodology): “Many methods [7, 13, 17, 19, 22, 23, 24, 32] have been proposed to tackle recommendation or collaborative filtering problems in the literature.”
SoRec: social recommendation using probabilistic matrix factorization (2008, background): “However, due to the nature of collaborative filtering, recommender systems based on this technique suffer from the following inherent weaknesses: (1) Due to the sparsity of the user-item rating matrix (the density of available ratings in commercial recommender systems is often less than 1% [19]), me”
A Comprehensive Survey of Neighborhood-based Recommendation Methods (2011, background): “For instance, in Significance Weighting [29, 49], a user similarity weight”
Recommender Systems (2016, unknown): “The significance-weighting methods of de-emphasizing the neighbors who have too few common ratings with a given neighbor are discussed in [71, 245, 247, 380].”
Collaborative reliability prediction of service-oriented systems (2010, methodology): “To take advantages of these collected data, this paper applies the collaborative filtering techniques [2, 18, 25, 39] to attack the challenging research problem of reliability prediction of service-oriented systems.”
EigenRank: a ranking-oriented approach to collaborative filtering (2008, methodology): “To alleviate such sparsity problem, different techniques have been proposed to fill in some of the unknown ratings in the matrix such as dimensionality reduction[8] and data-smoothing methods[25, 19].”