User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning
This addresses biased services in recommendation systems for users, though it is incremental as it builds on existing ensemble and clustering techniques.
The paper tackles the user skewed prediction problem in recommendation algorithms, where high average accuracy can mask poor performance for some users, by proposing a user-sensitive ensemble method (UREC) that clusters users and uses multi-task learning, showing superiority in experiments on real-world datasets.
This paper considers recommendation algorithm ensembles in a user-sensitive manner. Recently researchers have proposed various effective recommendation algorithms, which utilized different aspects of the data and different techniques. However, the "user skewed prediction" problem may exist for almost all recommendation algorithms -- algorithms with best average predictive accuracy may cover up that the algorithms may perform poorly for some part of users, which will lead to biased services in real scenarios. In this paper, we propose a user-sensitive ensemble method named "UREC" to address this issue. We first cluster users based on the recommendation predictions, then we use multi-task learning to learn the user-sensitive ensemble function for the users. In addition, to alleviate the negative effects of new user problem to clustering users, we propose an approximate approach based on a spectral relaxation. Experiments on real-world datasets demonstrate the superiority of our methods.