Document recommender agent based on hybrid approach

Rayner Alfred, and Patricia Anthony, and Khalifa Chekima, and Chin, Kim On (0151) Document recommender agent based on hybrid approach. International Journal of Machine Learning and Computing. pp. 151-156.

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Abstract

As Internet continues to grow, user tends to rely heavily on search engines. However, these search engines tend to generate a huge number of search results and potentially making it difficult for users to find the most relevant sites. This has resulted in search engines losing their usefulness. These users might be academicians who are searching for relevant academic papers within their interests. The need for a system that can assist in choosing the most relevant papers among the long list of results presented by search engines becomes crucial. In this paper, we propose Document Recommender Agent, that can recommend the most relevant papers based on the academician’s interest. This recommender agent adopts a hybrid recommendation approach. In this paper we also show that recommendation based on the proposed hybrid approach is better that the content-based and the collaborative approaches.

Item Type: Article
Uncontrolled Keywords: Document recommender agent, agent technology, information retrieval
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User: Unnamed user with email storage.bpmlib@ums.edu.my
Date Deposited: 17 Nov 2015 07:34
Last Modified: 12 Oct 2017 07:20
URI: http://eprints.ums.edu.my/id/eprint/12229

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