We investigate a new class of software for knowledge discovery in databases (KDD), called recommender systems. Recommender systems apply KDD-like techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-Commerce today. We extend previously studied KDD models to incorporate customer interaction so these mo...

This thesis consists of three papers on recommender systems. The first paper addresses the problem of making decentralized recommendations using a peer-to-peer architecture. Collaborating recommender agents are connected into a network of neighbors that exchange user recommendations to find new items to recommend. We achieved a performance comparable to a centralized system. The second paper de...

One of the main problems faced by university students is to create and manage the semester course plan. In this paper, we present a course enrollment recommender system based on data mining techniques. The system mainly helps with students’ enrollment decisions. More specifically, it provides recommendation of selective and optional courses with respect to students’ skills, knowledge, interests...

The plethora of content available to the consumer has become overwhelming. Increasing amounts of information are being disseminated through terrestrial broadcast, satellite, and cable leading to an information overload. Common modes of searching for TV programs currently in existence include: TV-guide, PreVue channel and rudimentary search tools available through satellite dish TV programming s...