Learning Relational User Profiles and Recommending Items as Their Preferences Change

Abstract

Over the last decade a vast number of businesses have developed online e-shops in the web. These online stores are supported by sophisticated systems that manage the products and record the activity of customers. There exist many research works that strive to answer the question “what items are the customers going to like” given their historical profiles. However, most of these works do not take into account the time dimension and cannot respond efficiently when data are huge. In this paper, we study the problem of recommendations in the context of multi-relational stream mining. Our algorithm “xStreams” first separates customers based on their historical data into clusters. It then employs collaborative filtering (CF) to recommend new items to the customers based on their group similarity. To evaluate the working of xStreams, we use a multi-relational data generator for streams. We evaluate xStreams on real and synthetic datasets.