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Discovering the impact of knowledge in recommender systems: a comparative study

Amini, Bahram and Ibrahim, Roliana and Othman, Mohd. Shahizan (2011) Discovering the impact of knowledge in recommender systems: a comparative study. International Journal of Computer Science & Engineering Survey (IJCSES), 2 (3). pp. 1-14. ISSN 0976-3252 (Print); 0976-2760 (Online)

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Official URL: http://dx.doi.org/10.5121/ijcses.2011.2301

Abstract

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.

Item Type:Article
Uncontrolled Keywords:Recommendation Systems, User Profile, Knowledge-based Recommender System, Semantic Web
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System
ID Code:39816
Deposited By: Fazli Masari
Deposited On:21 Jul 2014 05:28
Last Modified:17 Mar 2019 04:02

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