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Collaborative Topic Ranking: Leveraging Item Meta-data for Sparsity Reduction(Jing He, Victoria University)

Time: 2016-08-24 Views:677

主题:Collaborative Topic Ranking: Leveraging Item Meta-data for Sparsity Reduction(协同话题排序:借助项目元数据减少稀疏性)
主讲人:何静  博士
地点:计算机学院院楼 102会议室



Pair-wise ranking methods are popular for learning recommender systems from implicit feedback. However, user preferences and item characteristics cannot be estimated reliably due to overfitting given highly sparse data. To alleviate this problem, in this talk, I will introduce a novel hierarchical Bayesian framework which incorporates “bag-of-words” type meta-data on items into pair-wise ranking models for one-class collaborative filtering. The main idea of the method lies in extending the pair-wise ranking with a probabilistic topic modeling. Instead of regularizing item factors through a zero-mean Gaussian prior, our method introduces item-specific topic proportions as priors for item factors. As a by-product, interpretable latent factors for users and items may help explain recommendations in some applications. We conduct an experimental study on a real and publicly available dataset, and the results show that our algorithm is effective in providing accurate recommendation and interpreting user factors and item factors.