ACIRS: A Comprehensive Item Based Clustering Approach to Recommend Appropriate Items in Recommender System
Authors: 
Mahamudul Hasan, Farzana Aktar Lubna, Sadia Chowdhury, Nusrat Jarin Haque, Towsif Ahmed OmiAuthors Info & Claims
Pages 259 - 263
Abstract
Recommender system is that which collect users preferences and suggest the user some similar item that the user might like. It increases our interest in e-commerce sites by suggesting items. Recommender system suggests the appropriate items to the users based on his previous inclination. In clustering techniques, similar users or products are clustered together that can be used for a product recommendation. But it is difficult for an individual clustering algorithm to find a correlation that can help in recommendation. An item-based recommendation system has been proposed here based on clustering algorithms that can find the similarity between items. Item-Item based clustering algorithm first analyzes the user-item rating matrix to find out the relationship between different items and then use these relationships to compute recommendation for users. Finally, our experimental result provides good performance in terms of mean absolute error, precision, recall, and f-measures.
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- ACIRS: A Comprehensive Item Based Clustering Approach to Recommend Appropriate Items in Recommender System
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Published In
April 2019
267 pages
ISBN:9781450361064
DOI:10.1145/3330482
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Published: 19 April 2019
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ICCAI '19
ICCAI '19: 2019 5th International Conference on Computing and Artificial Intelligence
April 19 - 22, 2019
Bali, Indonesia
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