{"type":"ScholarlyArticle","title":"Improving Top-N Recommendation with Heterogeneous Loss","authors":[{"name":"Feipeng Zhao","slug":"feipeng-zhao","given":"Feipeng","family":"Zhao","affiliation":""},{"name":"Yuhong Guo","slug":"yuhong-guo","given":"Yuhong","family":"Guo","affiliation":""}],"abstract":"Personalized top-N recommendation systems have great impact on many real world applications such as E-commerce platforms and social networks. Most existing methods produce personalized top-N recommendations by minimizing a specific uniform loss such as pairwise ranking loss or pointwise recovery loss. In this paper, we propose a novel personalized top-N recommendation approach that minimizes a combined heterogeneous loss based on linear self-recovery models. The heterogeneous loss integrates the strengths of both pairwise ranking loss and pointwise recovery loss to provide more informative recommendation predictions. We formulate the learning problem with heterogeneous loss as a constrained convex minimization problem and develop a projected stochastic gradient descent optimization algorithm to solve it. We evaluate the proposed approach on a set of personalized top-N recommendation tasks. The experimental results show the proposed approach outperforms a number of state-of-the-art methods on top-N recommendation.","doi":null,"url":"https://ijcai.org/proceedings/2016/339","pdf":"https://ijcai.org/proceedings/2016/0339.pdf","markdown":"https://ijcai.org/proceedings/2016/339.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/339","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":339,"pages":{"first":2378,"last":2383},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}