The effectiveness of factorization and similarity blending

• URL: http://arxiv.org/abs/2209.13011v1
• Date: Fri, 16 Sep 2022 13:11:27 GMT
• Title: The effectiveness of factorization and similarity blending
• Authors: Andrea Pinto, Giacomo Camposampiero, Lo\"ic Houmard and Marc Lundwall
• Abstract summary: Collaborative Filtering (CF) is a technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. We show that blending factorization-based and similarity-based approaches can lead to a significant error decrease (-9.4%) on stand-alone models. We propose a novel extension of a similarity model, SCSR, which consistently reduce the complexity of the original algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative Filtering (CF) is a widely used technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. In this work, we illustrate our review of different CF techniques in the context of the Computational Intelligence Lab (CIL) CF project at ETH Z\"urich. After evaluating the performances of the individual models, we show that blending factorization-based and similarity-based approaches can lead to a significant error decrease (-9.4%) on the best-performing stand-alone model. Moreover, we propose a novel stochastic extension of a similarity model, SCSR, which consistently reduce the asymptotic complexity of the original algorithm.

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