1. Active Collaborative Filtering Machine Learning Group Department of Computer Science University of Toronto

2. Collaborative Filtering: Users express preference for items they have viewed, accessed, or purchased by assigning ratings to them. Collaborative filtering systems analyze the preference data to make customized recommendations and predictions for each user.

3. The Active Advantage: When a new user first joins a collaborative filtering system their rating profile is empty, and recommendations can be of poor quality. This is often called the New User Problem, and it affects all collaborative filtering systems. Our approach to Active Collaborative Filtering applies principled methods from decision theory to help overcome the new user problem by guiding the rating process.

4. Proven Results: Recent research has shown that our approach to ACF provides a significant improvement over entering ratings in a haphazard fashion. It also outperforms other methods that have been proposed in the past.

5. Proven Results: Improvement in Recommendation Quality (MCVQ)

6. Proven Results: Improvement in Recommendation Quality (NB)

7. Active Movie Recommendation Demo Includes 115 titles. Use the active query option or enter ratings manually. Top five list automatically recalculated. Fully interactive in real time.