Introducing linked open data in graph-based recommender systems

作者：

Highlights：

• We investigate the impact of the integration of the knowledge coming from the LOD cloud in a graph-based recommendation framework.

• We propose a methodology to automatically feed a graph-based recommendation algorithm with features coming from the LOD cloud.

• We give guidelines to drive the choice of the feature selection technique, according to the needs of a specic recommendation scenario (i.e., maximize accuracy, maximize diversity).

• We validate our methodology by evaluating its effectiveness with respect to several state-of-the-art datasets.

摘要

•We investigate the impact of the integration of the knowledge coming from the LOD cloud in a graph-based recommendation framework.•We propose a methodology to automatically feed a graph-based recommendation algorithm with features coming from the LOD cloud.•We give guidelines to drive the choice of the feature selection technique, according to the needs of a specic recommendation scenario (i.e., maximize accuracy, maximize diversity).•We validate our methodology by evaluating its effectiveness with respect to several state-of-the-art datasets.

论文关键词：Recommender systems,PageRank,Graphs,Linked open data,Feature selection,Diversity

论文评审过程：Received 5 August 2015, Revised 21 November 2016, Accepted 4 December 2016, Available online 10 December 2016, Version of Record 10 December 2016.

论文官网地址：https://doi.org/10.1016/j.ipm.2016.12.003