NDLI: Information Gain Clustering through Prototype-Embedded Genetic K-Mean Algorithm (IGCPGKA): a novel heuristic approach for personalisation of cold start problem

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Information Gain Clustering through Prototype-Embedded Genetic K-Mean Algorithm (IGCPGKA): a novel heuristic approach for personalisation of cold start problem

Content Provider	ACM Digital Library
Author	Al Jadaan, Omar Ramachandram, S. Hameed, Mohd Abdul
Abstract	Information Gain Clustering through Prototype - Embedded Genetic K-Mean Algorithm (IGCPGKA) is a novel heuristic used in Recommendation System (RS) for solving personalization problems. In a bid to generate information on the behavior and effectiveness of Prototype-Embedded Genetic K-mean Algorithm (PGKA) -- a clustering algorithm - in Recommender System (RS) used in personalization of cold start problem, IGCPGKA is proposed, developed and experimented upon in this work/paper. IGCPGKA is derived from IGCEGA (Information Gain Clustering through Elitizt Genetic Algorithm). The main difference between the two algorithms is articulated and exhibited in the clustering stage, and precisely, PGKA is used in IGCPGKA, while EGA (Elitizt Genetic Algorithm) is used in IGCEGA for clustering purposes. The effects of these differences have created positive results in terms of better recommendation for IGCPGKA and this fact is supported by the two evaluation metrics used in this work, namely Expected Utility (EU) and Mean Absolute Error (MAE). A comparison with other heuristics for personalization of cold start problem - such as Information Gain Clustering Neighbor through Bisecting K-Mean Algorithm (IGCN), Information Gain Clustering through Genetic Algorithm (IGCGA), entropy and popularity -- showed that IGCPGKA emerged vector by producing the best recommendation and this fact is also supported by the two evaluation metrics used in this work.
Starting Page	390
Ending Page	395
Page Count	6
File Format	PDF
ISBN	9781450313100
DOI	10.1145/2393216.2393282
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	K-mean operator (kmo) and genetic k-mean algorithm (gka) Expected utility (eu) Mean absolute error (mae) Recommendation system (rs) Personalization Web personalization Popularity Entropy
Content Type	Text
Resource Type	Article

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