Fajie Yuan, Guibing Guo, Joemon M. Jose, Long Chen, Haitao Yu, Weinan Zhang
2017 Proceedings of the 22nd International Conference on Intelligent User Interfaces - IUI '17  
Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases) rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which
more » ... grates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation.
doi:10.1145/3025171.3025211 dblp:conf/iui/YuanGJCYZ17 fatcat:n7mh6wxf4bgszjh2fesfsaoi6y