Leveraging High-Dimensional Side Information for Top-N Recommendation

TL;DR

This paper introduces a novel feature selection method for high-dimensional side information in top-N recommender systems, improving effectiveness and efficiency by learning sparse, non-negative feature weights.

Contribution

It proposes an embedded feature selection approach with learned feature weights, incorporating sparsity and non-negativity, optimized via ADMM for better recommendation performance.

Findings

Outperforms state-of-the-art methods in recommendation quality

Effectively filters out irrelevant high-dimensional features

Enhances efficiency in top-N recommendation tasks

Abstract

Top-$N$ recommender systems typically utilize side information to address the problem of data sparsity. As nowadays side information is growing towards high dimensionality, the performances of existing methods deteriorate in terms of both effectiveness and efficiency, which imposes a severe technical challenge. In order to take advantage of high-dimensional side information, we propose in this paper an embedded feature selection method to facilitate top-$N$ recommendation. In particular, we propose to learn feature weights of side information, where zero-valued features are naturally filtered out. We also introduce non-negativity and sparsity to the feature weights, to facilitate feature selection and encourage low-rank structure. Two optimization problems are accordingly put forward, respectively, where the feature selection is tightly or loosely coupled with the learning procedure. Augmented Lagrange Multiplier and Alternating Direction Method are applied to efficiently solve the problems. Experiment results demonstrate the superior recommendation quality of the proposed algorithm to that of the state-of-the-art alternatives.