Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

• DOI: 10.1145/3308558.3313411
• Publication Date: 2019-05-13T12:17:59Z
• Authors: Hongwei Wang; Fuzheng Zhang; Miao Zhao; Wenjie Li; Xing Xie; Minyi Guo
• Keywords: Knowledge graph; Feature (linguistics); Feature Learning

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