Dual-embedding based Neural Collaborative Filtering for Recommender Systems

1 code implementation • 4 Feb 2021 • Gongshan He, Dongxing Zhao, Lixin Ding

Inspired by the idea of SVD++ that represents users based on themselves and their interacted items, we propose a general collaborative filtering framework named DNCF, short for Dual-embedding based Neural Collaborative Filtering, to utilize historical interactions to enhance the representation.

Collaborative Filtering Recommendation Systems

Local Rademacher Complexity Bounds based on Covering Numbers

no code implementations • 6 Oct 2015 • Yunwen Lei, Lixin Ding, Yingzhou Bi

This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm.