Collaborative Filtering in Dynamic Streaming Environments

The increasing expansion of websites and their web usage necessitates increasingly scalable techniques for Web usage mining that can be better cast within the framework of mining evolving data streams [1, 5]. Despite recent developments in mining evolving Web clickstreams [3, 6], there has not been any investigation of the performance of collaborative filtering [2] in the demanding environment of evolving data streams. In this paper, we study limited memory collaborative filtering based recommendations in evolving scenarios using a systematic validation methodology.