KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-07-02
Print Publication Date2018
Permanent link to this recordhttp://hdl.handle.net/10754/628383
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AbstractRecommender systems with implicit feedback (e.g. clicks and purchases) suffer from two critical limitations: 1) imbalanced labels may mislead the learning process of the conventional models that assign balanced weights to the classes; and 2) outliers with large reconstruction errors may dominate the objective function by the conventional $L_2$-norm loss. To address these issues, we propose a robust asymmetric recommendation model. It integrates cost-sensitive learning with capped unilateral loss into a joint objective function, which can be optimized by an iteratively weighted approach. To reduce the computational cost of low-rank approximation, we exploit the dual characterization of the nuclear norm to derive a min-max optimization problem and design a subgradient algorithm without performing full SVD. Finally, promising empirical results demonstrate the effectiveness of our algorithm on benchmark recommendation datasets.
CitationYang P, Zhao P, Zheng VW, Ding L, Gao X (2018) Robust Asymmetric Recommendation via Min-Max Optimization. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR ’18. Available: http://dx.doi.org/10.1145/3209978.3210074.
JournalThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18