NDLI: IGCEGA: A Novel Heuristic Approach for Personalisation of Cold Start Problem

Content Provider	IEEE Xplore Digital Library
Author	Hameed, M.A. Ramachandram, S. Al Jadaan, O.
Copyright Year	2011
Abstract	IGCEGA, an acronym for Information Gain Clustering through Elitizt Genetic Algorithm, is a novel heuristic used in Recommender System (RS) for solving personalization problems. In comparison with IGCGA (Information Gain Clustering through Genetic Algorithm), IGCEGA is not associated with the inherent problem of increasing the possibility of losing good solution during the crossover phase, which translates into increasing the guarantee of converging to a global minima and consequently, enhancing the accuracy of the recommendation. Besides, IGCEGA using the technique of global minima still resolves the problem associated with IGCN (Information Gain through Clustered Neighbor), which traps the algorithm in local clustering centroids. Although this problem was alleviated by IGCGA, IGCEGA solves the problem even better because IGCEGA assumes the lowest Mean Absolute Error (MAE), the evaluation matrix used in this work. Results of the experimentation of the various heuristics / techniques in RS used in personalization for cold start problems -- for instance Popularity, Entropy, IGCN, IGCGA - showed that IGCEGA is associated with the lowest MAE, therefore, best clustering, which in turn results into best recommendation.
Starting Page	370
Ending Page	375
File Size	313083
Page Count	6
File Format	PDF
ISBN	9781457705434
DOI	10.1109/CSNT.2011.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-03
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Personalization Heuristic algorithms Popularity Entropy Biological cells Genetic algorithms Recommendation system Genetic algorithm (GA) Accuracy Mean absolute error Collaborative filtering Web personalization Clustering algorithms Elitist genetic algorithm (EGA) Bisecting k-mean algorithm
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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