Abstract. As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems-PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system.

Recommender systems research combines techniques from user modeling and information filtering in order to build search systems that are better able to respond to the preferences of individual users during the search for a particular item or product. Collaborative recommenders leverage the preferences of communities of similar users in order to guide the search for relevant items. The success of collaborative recommendation has always been restrained by the so-called sparsity problem, in which a lack of available user similarity knowledge ultimately limits the formation of high-quality user communities and has a subsequent impact on recommender accuracy. This article presents an approach to addressing the sparsity problem by describing and evaluating how implicit similarity knowledge can be discovered and exploited using data-mining techniques and an approach to recommendation that is inspired by case-based reasoning research.

No abstract

No abstract

No abstract