A Recommender System for Recommending Suitable Products in E-shop Using Explanations

Abstract

This article proposes a recommender system for recommending relevant products in e-shop using explanations. The proposed system consists of three recommender modules called VIEW, RATING, and PURCHASE. The recommender modules use a content-based filtering approach and a collaborative filtering approach. The proposed recommender system works with explanations that contain arguments why the system recommended the specific product. Based on these explanations the user sees why specific products are recommended by the system. The proposed system was experimentally verified and the results of the experimental verification are discussed.