Learning to Recommend Accurate and Diverse Items

Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, Hui Xiong
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as groundtruth for each user. We then represent each user or item as a vector resulted from the factorization of the user-item rating matrix. In our paper, we try to discover a factorization for matching the
more » ... ng supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled problems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state-of-the-art algorithms.
doi:10.1145/3038912.3052585 dblp:conf/www/ChengWMSX17 fatcat:7rlsg54qcjdknirmn3gmuu52vi