{"type":"ScholarlyArticle","title":"A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback","authors":[{"name":"Huayu Li","slug":"huayu-li","given":"Huayu","family":"Li","affiliation":""},{"name":"Richang Hong","slug":"richang-hong","given":"Richang","family":"Hong","affiliation":""},{"name":"Defu Lian","slug":"defu-lian","given":"Defu","family":"Lian","affiliation":""},{"name":"Zhiang Wu","slug":"zhiang-wu","given":"Zhiang","family":"Wu","affiliation":""},{"name":"Meng Wang","slug":"meng-wang","given":"Meng","family":"Wang","affiliation":""},{"name":"Yong Ge","slug":"yong-ge","given":"Yong","family":"Ge","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2016/241","pdf":"https://ijcai.org/proceedings/2016/0241.pdf","markdown":"https://ijcai.org/proceedings/2016/241.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/241","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":241,"pages":{"first":1683,"last":1688},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}