Abstract:Similar App recommendation is useful for helping users to discover their interested Apps.Different from existing similarity learning algorithms, the similar App recommendation focuses on presenting a ranking list of similar Apps for each App.In this paper, we put emphasis on how to learn a similarity function in a ranking scenario.Previous studies model the relative similarity in the form of triplets.Instead of triplets, we model the ranking list as a whole in the loss function, and propose a listwise multi-kernel similarity learning method, referred as SimListMKL.Experimental results on real world data set show that our proposed method SimListMKL outperforms the baselines approaches based on triplets.