Title: A method for solving cold start problem using market basket analysis
Authors: Nitin Mishra; Vimal Mishra; Saumya Chaturvedi
Addresses: Dr. A.P.J. Abdul Kalam Technical University, Sec-11, Jankipuram Vistar, Lucknow, Uttar Pradesh, India ' IERT Allahabad, 26, Chatham Line, Dharhariya, Prayagraj, Uttar Pradesh 211002, India ' Dr. A.P.J. Abdul Kalam Technical University, Sec-11, Jankipuram Vistar, Lucknow, Uttar Pradesh, India
Abstract: Recommendation system is the base of e-commerce business across the world. After the advent of 4G technology in developed and developing countries, people are using internet more than ever. Lot of options are available for almost everything on internet. Nowadays, screens have become smaller and data has become large. It has been observed that sometimes people leave the portal although information is there. Recommendation system makes this easier by giving users options on the basis of history of the user in the system. But, this recommendation system fails when we have no information about the user. In simple words, we did not have user history and we cannot use recommendation algorithm. In this paper, we are suggesting a market basket analysis (MBA) technique to help us solving this problem to some level. Market basket analysis technique has been used to determine popularity sequence of the movies.
Keywords: recommender systems; cold-start problem; market basket analysis; MBA; associative rule mining.
DOI: 10.1504/IJAIP.2024.139956
International Journal of Advanced Intelligence Paradigms, 2024 Vol.28 No.1/2, pp.155 - 168
Received: 28 Jul 2018
Accepted: 26 Oct 2018
Published online: 15 Jul 2024 *