.png){kind=icon}
.png){kind=icon}
Return
A recommendation approach for consuming linked open data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2016.10.037.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Most of linked open data (LOD) applications focus on the search and visualization of information, not efficiently using the links among objects in different data sources and the semantics of their relations. This work aims to create a LOD-consuming approach that uses recommendation techniques based on items' description, their relations, users' interests and social network. The proposed approach was instantiated by an application that uses movie related LOD. The results obtained in our experiments were promising: accuracy of the recommendations generated was equal or better, compared to other recommender algorithms used in conventional (not LOD) scenario. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Information retrieval
Linked open data
Recommender systems
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W