Semantic manifold modularization-based ranking for image recommendation
作者：
Highlights：
• The proposed MMR employs visual correlations of images that users consumed to reveal and infer users’ interests by interest propagation over the visual graph of images instead of propagating collaborative signals over users’ sparse interaction graph.
• We constrain manifold learning within visual groups adaptively to propagate users’ interests and prevent bias propagated across semantics as a tradeoff between personality and propagation smoothness.
• For image recommendation, the proposed MMR introduces manifold modularization to perform interest propagation in a decomposed manner and reduce computational burden exponentially.
摘要
•The proposed MMR employs visual correlations of images that users consumed to reveal and infer users’ interests by interest propagation over the visual graph of images instead of propagating collaborative signals over users’ sparse interaction graph.•We constrain manifold learning within visual groups adaptively to propagate users’ interests and prevent bias propagated across semantics as a tradeoff between personality and propagation smoothness.•For image recommendation, the proposed MMR introduces manifold modularization to perform interest propagation in a decomposed manner and reduce computational burden exponentially.
论文关键词：Manifold propagation,Modularization,Image recommendation,User interest
论文评审过程：Received 7 February 2020, Revised 10 April 2021, Accepted 2 June 2021, Available online 12 June 2021, Version of Record 12 July 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108100
