Preference Modeling by Exploiting Latent Components of Ratings

TL;DR

This paper introduces LCR, a novel recommendation method that decomposes user ratings into latent components using cost-sensitive learning, improving accuracy by modeling individual preferences more effectively.

Contribution

The paper proposes a new approach, LCR, which decomposes ratings into latent components through cost-sensitive learning, enhancing recommendation accuracy over existing methods.

Findings

LCR outperforms state-of-the-art methods on benchmark datasets.

Decomposition of ratings improves understanding of user preferences.

Cost-sensitive learning effectively updates latent factor models.

Abstract

Understanding user preference is essential to the optimization of recommender systems. As a feedback of user's taste, rating scores can directly reflect the preference of a given user to a given product. Uncovering the latent components of user ratings is thus of significant importance for learning user interests. In this paper, a new recommendation approach, called LCR, was proposed by investigating the latent components of user ratings. The basic idea is to decompose an existing rating into several components via a cost-sensitive learning strategy. Specifically, each rating is assigned to several latent factor models and each model is updated according to its predictive errors. Afterwards, these accumulated predictive errors of models are utilized to decompose a rating into several components, each of which is treated as an independent part to retrain the latent factor models. Finally, all latent factor models are combined linearly to estimate predictive ratings for users. In contrast to existing methods, LCR provides an intuitive preference modeling strategy via multiple component analysis at an individual perspective. Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-of-art methods in terms of recommendation accuracy.