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

本文提出了一种使用 AutoML 技术设计特定于数据的 CF 模型的新框架，其中关键是将 SOTA CF 方法统一为输入编码、嵌入函数、交互函数和预测函数的不相交阶段，以及使用随机搜索和性能预测器的易于使用、强大且高效的搜索策略。在五个真实数据集上的广泛实验表明，我们的方法可以在各种 CF 任务上始终优于 SOTA 方法，搜索到的 CF 模型还可以为探索更有效的方法提供启示。