Personalization using Memory-based CF approach for Marketing of Footwear

Abstract

Personalization systems compute recommendations based on user information. There are many kinds of personalization techniques such as collaborative filtering, content-based filtering, rulebased approach etc. Collaborative filtering (CF) has become an important data mining technique to make personalized recommendations for books, web pages or movies, etc. Collaborative filtering (CF) selects advertisement for customers based on the opinions of other customers with similar past preferences. Memory-based CF predicts users preference based on their similarity to other user in the database. This paper presents memory-based CF to predict for footwear advertisement. Pearson Correlation Coefficient Similarity algorithm is used to find the similarity between users.