AN IMPLEMENTATION OF PEARSON CORRELATION METHOD FOR PREDICTING ITEMS TO USER IN E-COMMERCE

Autor:	Arjun Singh Tomar*, Amit Srivastava, Shishir Kumar
Rok vydání:	2016
Předmět:	Data mining Data Filtration Collaborative Filtering Pearson correlation Recommender System
DOI:	10.5281/zenodo.57944
Popis:	Collaborative filtering algorithms (CFAs) are recommender systems for the collaborating one another to filter documents they read from last decade. CFAs have various features that create them different from other algorithms. Algorithm of user-based collaborative filtering is one of filtering algorithms, known for their effectiveness and simplicity. In the present paper, pearson calculation is applied which works on user-based data and finds out the similarity measure of the products and then recommend them according to the similarity calculated. An application is built to perform this research work whose web pages have been attached as a part of results calculated. The model is an improved model working well on collaborative technique and recommending items to the users. Along with this few add-on implementations have been done so as to improve the functionality of the application.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ddaea655bafa008b97422040e1f8b40e Zobrazit plný text záznamu