Nowadays, item-item recommendation plays an important role in modern recommender systems. Traditionally, this is either solved by behavior-based collaborative filtering or content-based methods. However, both kinds of methods often suffer from cold-start problems, or poor performance due to few behavior supervision; and hybrid methods which can leverage the strength of both kinds of methods are needed. In this paper, we propose a semi-parametric embedding framework for this problem. Specifically, the embedding of an item is composed of two parts, i.e., the parametric part from content information and the non-parametric part designed to encode behavior information; moreover, a deep learning algorithm is proposed to learn two parts simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of the proposed method.
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