Unifying user-based and item-based collaborative filtering approaches by similarity fusion

By Jun Wang, A. D. Vries, Marcel J. T. Reinders. 2006

975 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Transportation

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Citations in the corpus

Data Poisoning Attacks on Factorization-Based Collaborative Filtering (2016, methodology): “Existing approaches in the literature include nearest-neighbor methods, where a user’s (item’s) preference is determined by other users (items) with similar profiles [1], and factorization-based methods where the incomplete preference matrix is assumed to be approximately low-rank [2, 3].”
SoRec: social recommendation using probabilistic matrix factorization (2008, neutral): “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”
SAITS: Self-Attention-based Imputation for Time Series (2022, background): “Amounts of prior work are proposed to solve this problem with statistics and machine learning methods [2, 15, 16, 17, 18, 19, 20].”
Social Network Data Analytics (2011, methodology): “The NetClus algorithm introduced by Sun et al.[108] dealt with clustering networks with star network schema.”
Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games (2017, background): “Typical examples include the trading robots gaming on the stock markets [13], ad bidding agents competing with each other over online advertising exchanges [14], and e-commerce collaborative filtering recommenders [15] predicting user interests through the wisdom of the crowd [16].”
Exploiting geographical influence for collaborative point-of-interest recommendation (2011, methodology): “We propose to use a linear fusion framework to integrate ranked lists provided by the three above-mentioned recommenders into the final ranked list [4, 21].”
Recommender Systems (2012, methodology): “Hybrid collaborative filtering algorithms combining user, item-or attribute-based similarity were proposed [131,132].”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, background): “this approach view a dataset as a collection of user vectors with a specific number of dimensions, corresponding to items they rated [61, 143, 163].”
BRITS: Bidirectional Recurrent Imputation for Time Series (2018, neutral): “[30] imputed missing values in recommendation system with collaborative filtering.”
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models (2017, background): “Collaborative ltering is one of the most important methodologies for recommender systems, which explores underlying user-user or item-item similarity and based on which performs personalised recommendations [41].”
Interactive collaborative filtering (2013, methodology): “Major solutions are categorized into two classes: The memory-based meth-ods make the recommendation by explicitly modelling the user or item similarities [19, 12] or combining them together [38], while the model-based methods provide recommendations by developing a ‘model’ of user ratings.”
Comparison of collaborative filtering algorithms (2011, methodology): “Wang et al. [2006] proposed an alternative method which considers both betweenusers and between-items relations.”
Effective missing data prediction for collaborative filtering (2007, background): “…some large, fa- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.”
Learning to recommend with social trust ensemble (2009, background): “Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval] Information Filtering; J.4 [Computer Applications] Social and Behavioral Sciences General Terms: Algorithm, Experimentation Keywords: Recommender Systems, Social Network, Social Trust Ensemble, Matrix Factorization”
Recommender systems survey (2013, background): “[226] describe a system of justifications based on the features of users’ preference.”
Collaborative location and activity recommendations with GPS history data (2010, methodology): “We also follow [10] to provide a solution, UCF, which can use the additional information sources for unifying collaborative filtering.”
When large language models meet personalization: perspectives of challenges and opportunities (2023, neutral): “Traditionalsolutions,suchas matrixfactorization[10]anduser/item-basedapproaches[11],extractpotentiallyinteresting itemsbasedontheideathatuserswhohaveshownsimilarpreferencesinthepastarelikelyto havesimilarpreferencesinthefuture.”