2nd International eConference on Computer and Knowledge Engineering (ICCKE) , 2012-10-18

Title : ( A Multi-Criteria Hybrid Citation Recommendation System Based on Linked Data )

Authors: Fattane Zarrinkalam , mohsen kahani ,

Citation: BibTeX | EndNote

Citation recommendation systems can help a researcher find works that are relevant to his field of interest. Currently, most approaches in citation recommendation are based on a closed-world view which is limited to using a single data source for recommendation. Such a limitation decreases quality of the recommendations since no single data source contains all required information about different aspects of the literature. This paper proposes a citation recommendation approach based on the open-world view provided by the emerging web of data. It uses multiple linked data sources to create a rich background data layer, and a combination of content-based and multi-criteria collaborative filtering as the recommendation algorithm. Experiments demonstrate that the proposed approach is sound and promising.

Keywords

Recommendation; Linked Data; Enrichmen; Recommender Systems;
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@inproceedings{paperid:1031976,
author = {Zarrinkalam, Fattane and Kahani, Mohsen},
title = {A Multi-Criteria Hybrid Citation Recommendation System Based on Linked Data},
booktitle = {2nd International eConference on Computer and Knowledge Engineering (ICCKE)},
year = {2012},
location = {Mashhad, IRAN},
keywords = {Recommendation; Linked Data; Enrichmen; Recommender Systems; Multi-criteria},
}

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%0 Conference Proceedings
%T A Multi-Criteria Hybrid Citation Recommendation System Based on Linked Data
%A Zarrinkalam, Fattane
%A Kahani, Mohsen
%J 2nd International eConference on Computer and Knowledge Engineering (ICCKE)
%D 2012

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