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