Proceedings

A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback

Huayu Li, Richang Hong, Defu Lian, Zhiang Wu, Meng Wang, Yong Ge

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1683-1688.

Abstract

Implicit feedback based recommendation has recently been an important task with the accumulated user-item interaction data. However, it is very challenging to produce recommendations from implicit feedback due to the sparseness of data and the lack of negative feedback/rating. Although various factor models have been proposed to tackle this problem, they either focus on rating prediction that may lead to inaccurate top-k recommendations or are dependent on the sampling of negative feedback that often results in bias. To this end, we propose a Relaxed Ranking-based Factor Model, RRFM, to relax pairwise ranking into a SVM-like task, where positive and negative feedbacks are separated by the soft boundaries, and their non-separate property is employed to capture the characteristic of unobserved data. A smooth and scalable algorithm is developed to solve group- and instance- level's optimization and parameter estimation. Extensive experiments based on real-world datasets demonstrate the effectiveness and advantage of our approach.

Huayu Li, Richang Hong, Defu Lian, Zhiang Wu, Meng Wang, and Yong Ge. 2016. A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 1683–1688. https://ijcai.org/proceedings/2016/241
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