With the advent of e-commerce sites and social media, users express their preferences and tastes freely through user-generated content such as reviews and comments. In order to promote cross-selling, e-commerce sites such as eBay and Amazon regularly use such inputs from multiple domains and suggest items with which users may be interested. In this paper, we propose a topic coherence-based cross-domain recommender model. The core concept is to use topic modeling to extract topics from user-generated content such as reviews and combine them with reliable semantic coherence techniques to link different domains, using Wikipedia as a reference corpus. We experiment with different topic coherence methods such as pointwise mutual information (PMI) and explicit semantic analysis (ESA). Experimental results presented demonstrate that our approach, using PMI as topic coherence, yields 22.6% and using ESA yields 54.4% higher precision as compared with cross-domain recommender system based on semantic clustering.

中文翻译：

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丰富跨域推荐评论的主题连贯性

随着电子商务网站和社交媒体的出现，用户可以通过用户生成的内容（例如评论和评论）自由表达自己的喜好和品味。为了促进交叉销售，诸如eBay和Amazon之类的电子商务网站会定期使用来自多个域的此类输入，并建议用户可能感兴趣的项目。在本文中，我们提出了一个基于主题一致性的跨域推荐器模型。核心概念是使用主题建模从用户生成的内容（例如评论）中提取主题，并使用Wikipedia作为参考语料，将它们与可靠的语义一致性技术相结合，以链接不同的域。我们尝试使用不同的主题一致性方法，例如逐点相互信息（PMI）和显式语义分析（ESA）。实验结果表明，我们的方法，