研究生教育

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
时间：4月23日(本周四)下午4点
地点：计算机学院院楼 102会议室
 
详细内容：
 
        何静博士来自澳大利亚维多利亚大学工程与科学学院，主要研究方向为数据挖掘、人工智能、Web服务和Web搜索、时空数据库、多准则决策智能系统以及计算机在医疗、石油勘探与开发、水资源管理和电子商务领域的应用。目前已经在多个国际核心期刊和会议发表论文60余篇。并且受到了澳大利亚多个科研基金项目的资助。
        本次讲座，何静博士将介绍一种新的基于分层贝叶斯框架、采用“词袋”式的元数据项成对排序模型的协同过滤方法。该方法的主要思想是用概率主题模型扩展成对地排序。在一些公开的数据集上实验证明，该方法能够提供更准确的推荐，并且能够有些地解释用户因素和项目因素。
 
        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.
 
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