TACKLING THE COLD START PROBLEM IN RECOMMENDER SYSTEMS USING DATA MINING

Author Name: 1Guruvula Bhargava Deep, 2Chamarthi Srikrithi, 3Potu RachanaChowdary, 4Midde Lalith Mohan

Volume: 01 &  Issue:

Country: India

DOI NO.: 08.2020-25662434 DOI Link: http://www.doi-ds.org/doilink/01.2021-61591938/UIJIR

Affiliation:

¹Software Engineer,  Deepgrid Data Centre Pvt. Ltd, Telangana, India

²Software Developer, Deepgrid Data Centre Pvt. Ltd, Telangana, India

³B.Tech Graduate, GITAM University, Visakhapatnam, Andhra Pradesh, India

⁴B.Tech Graduate, GITAM University, Visakhapatnam, Andhra Pradesh, India

ABSTRACT

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. People often use recommender systems to make decision. Based on recommendations by other individuals or authorities, choices can be made even without adequate first-hand knowledge of the alternatives. In everyday life, we rely on recommendations from other people either by word of mouth; recommendation letters, movie and book reviews printed in newspapers, or general surveys etc. Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modelling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many online e-commerce applications such as Amazon.com, Netflix, and Pandora. Various personal services in business play important roles in the success of current marketing field. The personalized recommendation technique in recommender systems, one of the most important tools of personal service in websites, makes great significance in Internet marketing activities of e-Commerce. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas.

Key words: E-Commerce, Recommender System,Cold Start, Cross-Site Cold-Start Product Recommendation