CRAWLING THE ALGORITHMIC FOUNDATIONS OF RECOMMENDATION TECHNOLOGIES

The World-Wide-Web has emerged during the last decade as one of the most prominent research fields. However, its size, heterogeneity and complexity to a large extent overcome our ability to efficiently manipulate data using traditional techniques. In order to cope with these characteristics several Web applications require intelligent tools that may help to extract the proper information relevant to the user’s requests. In this thesis we report on the algorithmic aspects of recommendation technologies, which refer to algorithms and systems that have been developed to help users find items that may be of their interest from a variety of available items. Collaborative Filtering (CF), the prevalent method for providing recommendations, has been successfully adopted by research and industrial applications. However, its applicability is limited due to the sparsity and the scalability problems. Sparsity refers to a situation that transactional data are lacking or are insufficient, while scalability refers to the expensive computations required by CF. For addressing the scalability problem we propose a method of Incremental C