An efficient algorithm for web recommendation systems

Abstract

Different efforts have been made to address the problem of information overload on the Internet. Web recommendation systems based on web usage mining try to mine users' behavior patterns from web access logs, and recommend pages to the online user by matching the user's browsing behavior with the mined historical behavior patterns. In this paper we propose effective and scalable technique to solve the web page recommendation problem. We use distributed learning automata to learn the behavior of previous users' and cluster pages based on learned pattern. One of the challenging problems in recommendation systems is dealing with unvisited or newly added pages. As they would never be recommended, we need to provide an opportunity for these rarely visited or newly added pages to be included in the recommendation set. By considering this problem, and introducing a novel Weighted Association Rule mining algorithm, we present an algorithm for recommendation purpose. We employ the HITS algorithm to extend the recommendation set. We evaluate proposed algorithm under different settings and show how this method can improve the overall quality of web recommendations.