Representing conditional preference by boosted regression trees for recommendation
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- 作者：Juntao Liua ; prolay@163.com" class="auth_mail" title="E-mail the corresponding author ; Chenhong Suib ; sui6662008@163.com" class="auth_mail" title="E-mail the corresponding author ; Dewei Denga ; dengddw@163.com" class="auth_mail" title="E-mail the corresponding author ; Junwei Wanga ; wjw.20012001@163.com" class="auth_mail" title="E-mail the corresponding author ; Bin Fengb ; fengbin@mail.hust.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Wenyu Liub ; liuwy@hust.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Caihua Wu ; c ; wucaihua2009@163.com" class="auth_mail" title="E-mail the corresponding author ; wucaihua_1999@yahoo.com.cn" class="auth_mail" title="E-mail the corresponding author
- 关键词：Conditional preference ; Boosted regression trees ; Matrix factorization ; Collaborative filtering ; Recommender system
- 刊名：Information Sciences
- 出版年：2016
- 出版时间：10 January 2016
- 年：2016
- 卷：327
- 期：Complete
- 页码：1-20
- 全文大小：1274 K
文摘
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We prove that boosted regression trees is more expressive than linear and quadratic function for conditional preference.
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We propose to use boosted regression trees to represent conditional preference in recommender system.
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The proposed model is learned by combining gradient boost and coordinate descent.