Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2011
DOI: 10.1145/2020408.2020504
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Localized factor models for multi-context recommendation

Abstract: Combining correlated information from multiple contexts can significantly improve predictive accuracy in recommender problems. Such information from multiple contexts is often available in the form of several incomplete matrices spanning a set of entities like users, items, features, and so on. Existing methods simultaneously factorize these matrices by sharing a single set of factors for entities across all contexts. We show that such a strategy may introduce significant bias in estimates and propose a new mo… Show more

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Cited by 58 publications
(41 citation statements)
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“…Three main paradigms are proposed for this purpose. The first paradigm is "Regression Based Factor Models" (RBFM) and its extensions, proposed by Agarwal, Chen and colleagues [1,2,3,36], which have been successfully used in a variety of recommendation scenarios, such as social networks [33,34], professional networks and content recommendation. The basic idea behind RBFM is to replace zero-mean Gaussian distributions usually used in a simple LFM with regression-based means.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Three main paradigms are proposed for this purpose. The first paradigm is "Regression Based Factor Models" (RBFM) and its extensions, proposed by Agarwal, Chen and colleagues [1,2,3,36], which have been successfully used in a variety of recommendation scenarios, such as social networks [33,34], professional networks and content recommendation. The basic idea behind RBFM is to replace zero-mean Gaussian distributions usually used in a simple LFM with regression-based means.…”
Section: Related Workmentioning
confidence: 99%
“…An additional possibility is suggested in Agarwal and Chen [3] where a "global" representation is assumed. The "local" representation is drawn from the "global" representation by a multivariate normal distribution.…”
Section: Cofm Via Latent Space Regularizationmentioning
confidence: 99%
“…Suppose all the existing rating locations of the test set are denoted as Ω. Like many recent research papers [29,1], we also take the Root Mean Squared Error (RMSE) to measure the effectiveness of an algorithm on solving recommendation problems:…”
Section: Experiments On Recommendation Datamentioning
confidence: 99%
“…The general idea is to perform learning on each heterogeneous feature space independently and then summarize the results via ensemble. Recently, [7] proposes a recommendation model (collaborative filtering) that can combine information from different contexts. It finds a latent factor that connects all data sources, and propagate information through the latent factor.…”
Section: Related Workmentioning
confidence: 99%