Discovering the impact of knowledge in recommender systems: a comparative study
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....
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| Format: | Article |
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AIRCC Publishing
2011
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| _version_ | 1796857811332759552 |
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| author | Amini, Bahram Ibrahim, Roliana Othman, Mohd. Shahizan |
| author_facet | Amini, Bahram Ibrahim, Roliana Othman, Mohd. Shahizan |
| author_sort | Amini, Bahram |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T19:03:23Z |
| format | Article |
| id | utm.eprints-39816 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:03:23Z |
| publishDate | 2011 |
| publisher | AIRCC Publishing |
| record_format | dspace |
| spelling | utm.eprints-398162019-03-17T04:02:22Z http://eprints.utm.my/39816/ Discovering the impact of knowledge in recommender systems: a comparative study Amini, Bahram Ibrahim, Roliana Othman, Mohd. Shahizan QA75 Electronic computers. Computer science 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. AIRCC Publishing 2011 Article PeerReviewed 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) http://dx.doi.org/10.5121/ijcses.2011.2301 DOI :10.5121/ijcses.2011.2301 |
| spellingShingle | QA75 Electronic computers. Computer science Amini, Bahram Ibrahim, Roliana Othman, Mohd. Shahizan Discovering the impact of knowledge in recommender systems: a comparative study |
| title | Discovering the impact of knowledge in recommender systems: a comparative study |
| title_full | Discovering the impact of knowledge in recommender systems: a comparative study |
| title_fullStr | Discovering the impact of knowledge in recommender systems: a comparative study |
| title_full_unstemmed | Discovering the impact of knowledge in recommender systems: a comparative study |
| title_short | Discovering the impact of knowledge in recommender systems: a comparative study |
| title_sort | discovering the impact of knowledge in recommender systems a comparative study |
| topic | QA75 Electronic computers. Computer science |
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