Learning to Hash for Recommendation: A Survey

TL;DR

This survey reviews state-of-the-art hashing techniques for recommendation systems, focusing on architectures, search strategies, evaluation metrics, and future research directions to improve efficiency and scalability.

Contribution

It provides a comprehensive taxonomy and overview of HashRec algorithms, categorizing them by objectives, strategies, and scenarios, and discusses current limitations and future directions.

Findings

Categorized HashRec methods into a three-tier taxonomy.

Summarized evaluation metrics for effectiveness and efficiency.

Highlighted current limitations and future research directions.

Abstract

With the explosive growth of users and items, Recommender Systems are facing unprecedented challenges in terms of retrieval efficiency and storage overhead. Learning to Hash techniques have emerged as a promising solution to these issues by encoding high-dimensional data into compact hash codes. As a result, hashing-based recommendation methods (HashRec) have garnered growing attention for enabling large-scale and efficient recommendation services. This survey provides a comprehensive overview of state-of-the-art HashRec algorithms. Specifically, we begin by introducing the common two-tower architecture used in the recall stage and by detailing two predominant hash search strategies. Then, we categorize existing works into a three-tier taxonomy based on: (i) learning objectives, (ii) optimization strategies, and (iii) recommendation scenarios. Additionally, we summarize widely adopted evaluation metrics for assessing both the effectiveness and efficiency of HashRec algorithms. Finally, we discuss current limitations in the field and outline promising directions for future research. We index these HashRec methods at the repository \href{https://github.com/Luo-Fangyuan/HashRec}{https://github.com/Luo-Fangyuan/HashRec}.