A Novel Top-N Recommendation Approach Based on Conditional Variational Auto-Encoder
Personalized recommendation has continuously received attention due to its great commercial value in business.Recently variational auto-encoder is employed in top-N recommendation for its effectiveness in deep collaborative filtering.The key challenge of model-based collaborative filtering is to develop effective latent factors representations with user-item interaction records.In this paper,we present a new class of conditional variational auto-encoders(CVAEs)that utilizes the fact of similar users tending to associate with each other on purchasing preference.This type of conditional variational auto-encoder concentrates on learning with label verification signals to ensure an exclusive latent mean factor for users with the same labels.Moreover,to handle complex multilabel combinations,we extend the model with a split-merge framework by learning labels of different conditional attributes separately and then merge the results from multiple prediction pools.Extensive experiments are conducted on two real-life datasets to simulate both user-based and item-based recommendation scenarios.Experimental results are favorable when comparing with the state-of-art methods.
Recommender systems Collaborative filtering Variational auto-encoder
Bo Pang Min Yang Chongjun Wang
State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing,China Software Institute,Jilin University,Jilin,China
国际会议
澳门
英文
357-368
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）