Learning Personalized Topical Compositions with Item Response Theory
Learning Personalized Topical Compositions with Item Response Theory
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DOI：
10.1145/3289600.3291022
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Lu Lin;Lin Gong;Hongning Wang
中科院分区：
文献类型：
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作者：
Lu Lin;Lin Gong;Hongning Wang
用户生成的评论文档是项目的内在属性和用户感知的这些属性的组成之间的产物。如果不对这两个因素进行适当的建模和解耦，就很难从这样的用户生成的数据中获得任何准确的用户理解或项目分析。在本文中，我们研究了一个新的文本挖掘问题，旨在区分用户的主观组成的主题内容在他/她的审查文件从实体的内在属性。受项目反应理论（IRT）的启发，我们将每个评论文档建模为用户对项目的详细反应，并假设该反应由用户的个性和项目的属性共同决定。我们使用生成式主题模型对基于文本的响应进行建模，在生成式主题模型中，我们在低维主题空间中描述了项目的属性和用户对它们的表现。通过后验推理，我们分离和研究这两个组成部分的审查文件的集合。对两个大型Amazon和Yelp评论数据集的广泛实验验证了所提出的解决方案的有效性：它优于最先进的主题模型，在看不见的文档中具有更好的预测能力，这直接转化为项目推荐和项目摘要任务的性能提高。
A user-generated review document is a product between the item's intrinsic properties and the user's perceived composition of those properties. Without properly modeling and decoupling these two factors, one can hardly obtain any accurate user understanding nor item profiling from such user-generated data. In this paper, we study a new text mining problem that aims at differentiating a user's subjective composition of topical content in his/her review document from the entity's intrinsic properties. Motivated by the Item Response Theory (IRT), we model each review document as a user's detailed response to an item, and assume the response is jointly determined by the individuality of the user and the property of the item. We model the text-based response with a generative topic model, in which we characterize the items' properties and users' manifestations of them in a low-dimensional topic space. Via posterior inference, we separate and study these two components over a collection of review documents. Extensive experiments on two large collections of Amazon and Yelp review data verified the effectiveness of the proposed solution: it outperforms the state-of-art topic models with better predictive power in unseen documents, which is directly translated into improved performance in item recommendation and item summarization tasks.