Collaborative Rating Allocation

Collaborative Rating Allocation

Yali Du, Chang Xu, Dacheng Tao

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1617-1623. https://doi.org/10.24963/ijcai.2017/224

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.
Keywords:
Machine Learning: Feature Selection/Construction
Machine Learning: Learning Preferences or Rankings
Machine Learning: Machine Learning