2015
DOI: 10.1108/lht-06-2015-0063
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Recommending research articles using citation data

Abstract: This study compares some of the characteristic differences among recommendations generated by a citation-based recommender and a user-based recommender for research articles. As with other application domains, the application of collaborative filtering methods for recommending items in a digital library suffers from a sparsity of usage data. One method for addressing this sparsity problem is to employ an article's references as a proxy for co-download information. Another method is to harvest large quantities … Show more

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Cited by 11 publications
(4 citation statements)
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References 27 publications
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“…Since this three-year period, the total number of citations of the total articles has been decreasing with 7281 in the following period and with 2816 in the last triennium. This is due to the fact that the published articles, or those corresponding to the last 6 years, will receive a greater number of citations in the coming years due to their recent publication and impact and their distribution in open access [138,139]. This is related to the average annual number of citations per item.…”
Section: Distribution Of Publications By Subject Area and Journalmentioning
confidence: 99%
“…Since this three-year period, the total number of citations of the total articles has been decreasing with 7281 in the following period and with 2816 in the last triennium. This is due to the fact that the published articles, or those corresponding to the last 6 years, will receive a greater number of citations in the coming years due to their recent publication and impact and their distribution in open access [138,139]. This is related to the average annual number of citations per item.…”
Section: Distribution Of Publications By Subject Area and Journalmentioning
confidence: 99%
“…Other recommender systems are based on a collaborative filtering (CF) approach. CF systems are based on analyzing user behavior, assuming that predictions can be made by considering users' past decisions and comparing them to other users (Schafer et al, 2007;Vellino, 2015). CF operates by comparing user behaviors, purchases, ratings, and preferences to identify similarities and make recommendations.…”
Section: Definitions Approaches and Application Domainsmentioning
confidence: 99%
“…Like the recommendation techniques of CBF, CF needs to know users' interests, which is especially effective for recommending related papers, even without content-based features [50]. The basic idea of CF is that if users A and B make ratings on some common items, their interests will be considered similar.…”
Section: B Collaborative Filtering (Cf)mentioning
confidence: 99%