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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(2): 151-156 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.404

Document Recommender Agent Based on Hybrid Approach

Khalifa Chekima, Chin Kim On, Rayner Alfred, and Patricia Anthony
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.

Index Terms—Document recommender agent, agent technology, information retrieval.

Khalifa Chekima, Chin Kim On, Rayner Alfred are with the Center of Excellence in Semantic Agents, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia (e-mail: k.chekima@gmail.com; kimonchin@ums.edu.my; ralfred@ums.edu.my).
Patricia Anthony is with the Department of Applied Computing, Faculty of Environment, Society and Design, Lincoln University, Christchurch, New Zealand (e-mail: patricia.anthony@lincoln.ac.nz).

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Cite: Khalifa Chekima, Chin Kim On, Rayner Alfred, and Patricia Anthony, "Document Recommender Agent Based on Hybrid Approach," International Journal of Machine Learning and Computing vol.4, no. 2, pp. 151-156, 2014.

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