Boosting Simple Collaborative Filtering Models Using Ensemble Methods
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogene...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
13.11.2012
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Subjects | |
Online Access | Get full text |
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Summary: | In this paper we examine the effect of applying ensemble learning to the
performance of collaborative filtering methods. We present several systematic
approaches for generating an ensemble of collaborative filtering models based
on a single collaborative filtering algorithm (single-model or homogeneous
ensemble). We present an adaptation of several popular ensemble techniques in
machine learning for the collaborative filtering domain, including bagging,
boosting, fusion and randomness injection. We evaluate the proposed approach on
several types of collaborative filtering base models: k- NN, matrix
factorization and a neighborhood matrix factorization model. Empirical
evaluation shows a prediction improvement compared to all base CF algorithms.
In particular, we show that the performance of an ensemble of simple (weak) CF
models such as k-NN is competitive compared with a single strong CF model (such
as matrix factorization) while requiring an order of magnitude less
computational cost. |
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DOI: | 10.48550/arxiv.1211.2891 |