Efficient Data-specific Model Search for Collaborative Filtering

TL;DR

This paper introduces an AutoML-based framework for designing data-specific collaborative filtering models, unifying existing methods and efficiently searching for optimal configurations, leading to improved performance on real-world datasets.

Contribution

The paper proposes a novel AutoML framework that unifies and automates the design of data-specific CF models, enhancing flexibility and performance.

Findings

Outperforms state-of-the-art CF methods on five datasets

Efficient search strategy reduces computational cost

Provides insights for future CF model development

Abstract

Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences towards items. However, designing a proper CF model for a given data is not easy, since the properties of datasets are highly diverse. In this paper, motivated by the recent advances in automated machine learning (AutoML), we propose to design a data-specific CF model by AutoML techniques. The key here is a new framework that unifies state-of-the-art (SOTA) CF methods and splits them into disjoint stages of input encoding, embedding function, interaction function, and prediction function. We further develop an easy-to-use, robust, and efficient search strategy, which utilizes random search and a performance predictor for efficient searching within the above framework. In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones. Extensive experiments on five real-world datasets demonstrate that our method can consistently outperform SOTA ones for various CF tasks. Further experiments verify the rationality of the proposed framework and the efficiency of the search strategy. The searched CF models can also provide insights for exploring more effective methods in the future