Mixed similarity learning for recommendation with implicit feedback

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• 作者：Mengsi Liu ^{liumengsi@email.szu.edu.cn} ; Weike Pan¹ ; ^{panweike@szu.edu.cn} ; Miao Liu ^{liumiao1@email.szu.edu.cn} ; Yaofeng Chen ^{chenyaofeng@email.szu.edu.cn} ; Xiaogang Peng ; ^{pengxg@szu.edu.cn} ; Zhong Ming ; ^{mingz@szu.edu.cn}
• 关键词：Mixed similarity ; Implicit feedback ; Recommender systems
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：1 March 2017
• 年：2017
• 卷：119
• 期：Complete
• 页码：178-185
• 全文大小：850 K
• 卷排序：119

文摘

We study an important recommendation problem with implicit feedback from the perspective of item similarity. We exploit the complementarity of the predefined similarity and the learned similarity via a novel mixed similarity model. We develop a novel recommendation algorithm, i.e., pairwise factored mixed similarity model (P-FMSM), based on the mixed similarity and pairwise preference assumption. We showcase the effectiveness of P-FMSM as compared with several state-of-the-art methods on four public datasets.