We demonstrate a method for collaborative ranking of future events. Previous work on
recommender systems typically relies on feedback on a particular item, such as a movie …

An ever increasing number of social services offer thousands of diverse events per day.
Users tend to be overwhelmed by the massive amount of information available, especially …

This paper presents a new approach for memory-based collaborative filtering algorithms. In
general, user-based rating prediction is a process in which each neighbor suggests a rating …

We describe a recommender system which uses a unique combination of content-based
and collaborative methods to suggest items of interest to users, and also to learn and exploit …

In conversational collaborative recommender systems, user feedback influences the
recommendations. We report mechanisms for enhancing the diversity of the …

Burgeoning information volume on the internet has created increasing interest in automated
filtering, refinement and presentation of relevant information to users. Recommender …

Research and development of recommender systems has been a vibrant field for over a
decade, having produced proven methods for “preference-aware” computing …

The continued growth of online content makes personalized recommendation an
increasingly important tool for media consumption. While collaborative filtering techniques …

Traditionally, collaborative recommender systems have been based on a single-shot model
of recommendation where a single set of recommendations is generated based on a user's …

Our research agenda focuses on building software agents that can employ user modeling
techniques to facilitate information access and management tasks. Personal assistant …