Mixed similarity learning for recommendation with implicit feedback
作者:
Highlights:
• 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.
摘要
•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.
论文关键词:Mixed similarity,Implicit feedback,Recommender systems
论文评审过程:Received 13 June 2016, Revised 8 December 2016, Accepted 9 December 2016, Available online 14 December 2016, Version of Record 25 January 2017.
论文官网地址:https://doi.org/10.1016/j.knosys.2016.12.010