Group-based Latent Dirichlet Allocation (Group-LDA): Effective audience detection for books in online social media

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• 作者：Peng Zhang^a ; ^b ; ^{zhpll@126.com" class="auth_mail" title="E-mail the corresponding author} ; Hansu Gu ; ^c ; ^{guhansu@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Mike Gartrell^d ; Tun Lu ; ^a ; ^b ; ^{lutun@fudan.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Dayi Yang^a ; ^b ; Xianghua Ding^a ; ^b ; Ning Gu^a ; ^b
• 关键词：Social media ; Book recommendation and marketing ; Audience detection ; Group-LDA
• 刊名：Knowledge-Based Systems
• 出版年：2016
• 出版时间：1 August 2016
• 年：2016
• 卷：105
• 期：Complete
• 页码：134-146
• 全文大小：1785 K

文摘

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 of group to describe the topic relevance among documents by incorporating book module and book chapter information into the model. Group-LDA is evaluated on Weibo.com with fifty popular books randomly sampled from the reading channel on Douban.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.