  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.
