Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems

TL;DR

This paper introduces a Multi-Relational Memory Network that leverages multiple types of user feedback to improve personalized recommendations across various online platforms.

Contribution

It presents a novel neural framework that models fine-grained user-item relations and discriminates feedback types, enhancing recommendation accuracy.

Findings

MRMN outperforms state-of-the-art algorithms in diverse scenarios.

Incorporating multiple feedback types improves personalization.

The model effectively captures user preferences from various interaction types.

Abstract

The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.