Active Collaborative Filtering. Machine Learning Group Department of Computer Science University of Toronto. Users express preference for items they have viewed, accessed, or purchased by assigning ratings to them.
Machine Learning Group
Department of Computer ScienceUniversity of Toronto
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 customizedrecommendationsandpredictions for each user.
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 itaffects all collaborative filtering systems.
Our approach to Active Collaborative Filtering applies principled methods from decision theoryto help overcome the new user problem by guiding the rating process.
The Active Advantage:
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.
Improvement in Recommendation Quality (MCVQ)
Improvement in Recommendation Quality (NB)
Includes 115 titles.
Fully interactive in real time.
Use the active query option or enter ratings manually.
Top five list automatically recalculated.