This paper is published in Volume-3, Issue-3, 2017
Area
Computer Science
Author
Ramandeep Sharma, Samarth Kapoor
Org/Univ
Swami Devi Dyal Institute of Engineering & Technology, Panchkula, Haryana, India
Keywords
Product Recommendations, K-Nearest Neighbor, E-Commerce Ranking, Accessibility, Pattern Recognition
Citations
IEEE
Ramandeep Sharma, Samarth Kapoor. A Review on the Page Recommendation Model using Machine Learning Approaches, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Ramandeep Sharma, Samarth Kapoor (2017). A Review on the Page Recommendation Model using Machine Learning Approaches. International Journal of Advance Research, Ideas and Innovations in Technology, 3(3) www.IJARIIT.com.
MLA
Ramandeep Sharma, Samarth Kapoor. "A Review on the Page Recommendation Model using Machine Learning Approaches." International Journal of Advance Research, Ideas and Innovations in Technology 3.3 (2017). www.IJARIIT.com.
Ramandeep Sharma, Samarth Kapoor. A Review on the Page Recommendation Model using Machine Learning Approaches, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Ramandeep Sharma, Samarth Kapoor (2017). A Review on the Page Recommendation Model using Machine Learning Approaches. International Journal of Advance Research, Ideas and Innovations in Technology, 3(3) www.IJARIIT.com.
MLA
Ramandeep Sharma, Samarth Kapoor. "A Review on the Page Recommendation Model using Machine Learning Approaches." International Journal of Advance Research, Ideas and Innovations in Technology 3.3 (2017). www.IJARIIT.com.
Abstract
The word “page or product range” means to rank the system to display the results and to provide suggestions to others. In day to day activities we rely on E-commerce ranking system such as the page indexing or product ranking of movies like reviews of movies from newspaper etc These E-commerce ranking basically help us to decide what we should opt for when we have multiple options An E-commerce ranking system (RS) helps people that have not sufficient personal experience or competence to evaluate the, potentially overwhelming, number of alternatives offered by a Web site In their simplest form RSs recommend to their users personalized and ranked lists of items Provide consumers with information to help them decide which items to purchase. In this paper, we have propsoed the improved model for the purpose of product recommendation with the higher accuracy and optimized elapsed time based parameters.