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As users often express their preferences with binary behavior data~(implicitfeedback), such as clicking items or buying products, implicit feedback basedCollaborative Filtering~(CF) models predict the top ranked items a user mightlike by leveraging implicit user-item interaction data. For each user, theimplicit feedback is divided into two sets: an observed item set with limitedobserved behaviors, and a large unobserved item set that is mixed with negativeitem behaviors and unknown behaviors. Given any user preference predictionmodel, researchers either designed ranking based optimization goals or reliedon negative item mining techniques for better optimization. Despite theperformance gain of these implicit feedback based models, the recommendationresults are still far from satisfactory due to the sparsity of the observeditem set for each user. To this end, in this paper, we explore the uniquecharacteristics of the implicit feedback and propose Set2setRank framework forrecommendation. The optimization criteria of Set2setRank are two folds: First,we design an item to an item set comparison that encourages each observed itemfrom the sampled observed set is ranked higher than any unobserved item fromthe sampled unobserved set. Second, we model set level comparison thatencourages a margin between the distance summarized from the observed item setand the most "hard" unobserved item from the sampled negative set. Further, anadaptive sampling technique is designed to implement these two goals. We haveto note that our proposed framework is model-agnostic and can be easily appliedto most recommendation prediction approaches, and is time efficient inpractice. Finally, extensive experiments on three real-world datasetsdemonstrate the superiority of our proposed approach.

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation