Robust Asymmetric Recommendation via Min-Max Optimization

Peng Yang, Peilin Zhao, Vincent W. Zheng, Lizhong Ding, Xin Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Recommender 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.
Original languageEnglish (US)
Title of host publicationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Print)9781450356572
StatePublished - Jul 2 2018


Dive into the research topics of 'Robust Asymmetric Recommendation via Min-Max Optimization'. Together they form a unique fingerprint.

Cite this