Group-based Latent Dirichlet Allocation (Group-LDA): Effective audience detection for books in online social media
Group-based Latent Dirichlet Allocation (Group-LDA): Effective audience detection for books in online social media
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DOI：
10.1016/j.knosys.2016.05.006
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
P. Zhang;Hansu Gu;Mike Gartrell;T. Lu;Dayi Yang;X. Ding;Ning Gu
中科院分区：
文献类型：
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作者：
P. Zhang;Hansu Gu;Mike Gartrell;T. Lu;Dayi Yang;X. Ding;Ning Gu
目前在线社交媒体上的大多数图书推荐和营销策略都是通过为品牌创建话题或发布广告来实现的。他们没有准确地瞄准对这些书感兴趣的受众，因此推荐或营销质量得不到保证。为了解决这一问题，我们提出了一种基于分组的潜在狄利克雷分配(Group-LDA)的有效受众检测方法，以准确地检测图书受众。GROUP-LDA是在潜在狄利克雷分配(LDA)的基础上发展而来的一种新的概率主题模型，通过在模型中加入图书模块和图书章节信息，引入了潜在的组概念来描述文档之间的主题相关性。在微博上，从豆瓣上的阅读频道随机抽取了50本畅销书，对Group-LDA进行了评估。根据评估结果，Group-LDA能够有效地检测出大多数类别的图书的不同类型的读者。在图书受众检测的查准率、查全率、F1-Score和MAP等方面，它都优于LSA、LDA、作者-主题模型(ATM)和其他一些协同过滤方法。
Most current book recommendation and marketing strategies in online social media are implemented by creating topics or posting advertisements for the brand. They do not precisely target the audiences who are interested in these books, so the recommendation or marketing quality is not guaranteed. In order to solve this problem, we propose an effective audience detection method based on Group-based Latent Dirichlet Allocation (Group-LDA) in order to precisely detect book audiences. Group-LDA is a new probabilistic topic model derived from Latent Dirichlet Allocation (LDA), which introduces a new latent concept ofgroupto describe the topic relevance among documents by incorporating book module and book chapter information into the model. Group-LDA is evaluated onWeibo.comwith fifty popular books randomly sampled from the reading channel onDouban.com. According to the evaluation results, Group-LDA can effectively detect different types of readers for most categories of books. It outperforms LSA, LDA, author-topic model (ATM) and some other collaborative filtering methods in terms of precision, recall, F1-score and MAP for book audience detection.