Collaborative Recommender Systems for Online Shops

Abstract

Recommender systems are often used in electronic shops in order to suggest similar or related products, potentially interesting products for a given customer or a set of products for a marketing campaign. Most recommender systems use the collaborative filtering method in order to provide the personalization information. The collaborative filtering method is a very efficient and convenient way of achieving personalization as there is no need to introduce semantic information about the products or to manually link products and users together. In the last years, a number of optimizations for collaborative filtering techniques have been developed. This paper collects the ideas and shows which of them could be integrated successfully in order to optimize a collaborative recommender system for online shops.