A Recommendation System for Repetitively Purchasing Items in E-commerce Based on Collaborative Filtering and Association Rules

In this paper, we are to address the problem of item recommendations to users in shopping malls selling several different kinds of items, e.g., daily necessities such as cosmetics, detergent, and food ingredients. Most of current recommendation algorithms are developed for sites selling only one kind of items, e.g., music or movies. To devise efficient recommendation algorithms suitable for repetitively purchasing items, we give a method to implicitly assign ratings for these items by making use of repetitive purchase counts, and then use these ratings for the purpose of recommendation prediction with the help of user-based collaborative filtering and item-based collaborative filtering algorithms. We also propose associate item-based recommendation algorithm. Items are called associate items if they are frequently bought by users at the same time. If a user is to buy some item, it is reasonable to recommend some of its associate items. We implement user-based (item-based) collaborative filtering algorithm and associate item-based algorithm, and compare these three algorithms in view of the recommendation hit ratio, prediction performance, and recommendation coverage, along with computation time.