Fast recommendation on latent collaborative relations

作者：

Highlights：

• Devise a recommendation algorithm based on latent factor model.

• Combines latent factors and ℓ2 norm to formulate the recommendation problem as a k-nearest-neighbor problem, in which we further use locality sensitive hashing (LSH) to reduce search time complexity.

• Speedup the retrieval by 5X –313X on three data sets used in the experiments.

摘要

•Devise a recommendation algorithm based on latent factor model.•Combines latent factors and ℓ2 norm to formulate the recommendation problem as a k-nearest-neighbor problem, in which we further use locality sensitive hashing (LSH) to reduce search time complexity.•Speedup the retrieval by 5X –313X on three data sets used in the experiments.

论文关键词：Recommender systems,Latent factor model,Locality-sensitive hashing,Nearest neighbors

论文评审过程：Received 28 January 2016, Revised 1 June 2016, Accepted 13 June 2016, Available online 18 June 2016, Version of Record 3 September 2016.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.06.016