Learning recency based comparative choice towards point-of-interest recommendation

作者：

Highlights：

• We explore auxiliary resource-time stamps of ratings for POI recommendation.

• We consider partial order between ratings rather than their numeric values.

• We novelly model user behaviors by incorporating comparative choice.

• We devise a stochastic gradient descent algorithm via collection-wise learning.

• Experiments on two real datasets show our method outperform other method.

摘要

•We explore auxiliary resource-time stamps of ratings for POI recommendation.•We consider partial order between ratings rather than their numeric values.•We novelly model user behaviors by incorporating comparative choice.•We devise a stochastic gradient descent algorithm via collection-wise learning.•Experiments on two real datasets show our method outperform other method.

论文关键词：Points-of-Interest,Recommendation,Choice model,Partial order,Learning algorithm

论文评审过程：Available online 4 February 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.01.054