Title: Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation,
Abstract: Collaborative filtering often suffers from sparsity and cold start problems
in real recommendation scenarios, therefore, researchers and engineers usually
use side information to address the issues and improve the performance of
recommender systems. In this paper, we consider knowledge graphs as the source
of side information. We propose MKR, a Multi-task feature learning approach for
Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework
that utilizes knowledge graph embedding task to assist recommendation task. The
two tasks are associated by cross&compress units, which automatically share
latent features and learn high-order interactions between items in recommender
systems and entities in the knowledge graph. We prove that cross&compress units
have sufficient capability of polynomial approximation, and show that MKR is a
generalized framework over several representative methods of recommender
systems and multi-task learning. Through extensive experiments on real-world
datasets, we demonstrate that MKR achieves substantial gains in movie, book,
music, and news recommendation, over state-of-the-art baselines. MKR is also
shown to be able to maintain a decent performance even if user-item
interactions are sparse.