Recommender Systems in ECommerce

Rafeh, Reza (2017) Recommender Systems in ECommerce. Research in Action event - showcasing the innovative research that we are doing at Wintec in our Centre for Business, Information Technology and Enterprise, and in the wider Waikato, Wintec, 20 November 2017.

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Abstract or Summary

E-commerce is growing rapidly offering a vast number of products and services to the users. Facing with a wide range of options, users cannot decide which one would be the most suitable option. Recommender systems help users to find the most suitable item easier and faster. To do this, recommender systems apply machine learning algorithms to user’s data to build sophisticated models to predict the user’s behavior in the future. There are many recommender systems employed by companies to increase their profitability. Some examples include Amazon, Movielens, Youtube, Facebook, and Linkedin. This presentation details the implementation of a cluster based recommender system that can accurately recommend items to users.

Item Type:Item presented at a conference, workshop or other event which was not published in the proceedings
Keywords that describe the item:Recommender systems, Collaborative Filtering, Clustering; Genetic Algorithm
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions:Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
ID Code:5618
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Deposited On:01 Mar 2018 03:10
Last Modified:01 Mar 2018 03:10

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