Collaborative Rating Allocation

This paper studies the collaborative rating allocation problem, in which each user has limited ratings on all items. These users are termed ``energy limited''. Different from existing methods which treat each rating independently, we investigate the geometric properties of a user's rating vector, and design a matrix completion method on the simplex. In this method, a user's rating vector is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean metric, a non-linear pull-back distance measurement from the sphere is adopted since it can depict the geometric constraints on each user's rating vector. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model's competitiveness versus other collaborative rating prediction methods.

Authors

• Yali Du King's College London
• Chang Xu University of Sydney
• Dacheng Tao College of Computing and Data Science, Nanyang Technological University

Keywords

• Machine Learning: Feature Selection/Construction
• Machine Learning: Learning Preferences or Rankings
• Machine Learning: Machine Learning