Kernel meets recommender systems : A multi-kernel interpolation for matrix completion

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
View graph of relations


  • Zhaoliang Chen
  • Wei Zhao
  • Shiping Wang


Original languageEnglish
Article number114436
Journal / PublicationExpert Systems with Applications
Online published3 Dec 2020
Publication statusPublished - 15 Apr 2021


A primary research direction for recommender systems is matrix completion, which attempts to recover the missing values in a user–item rating matrix. There are numerous approaches for rating tasks, which are mainly classified into latent factor models and neighborhood-based models. Most neighborhood-based models seek similar neighbors by computing similarities in the original data space for final predictions. In this paper, we propose a new neighborhood-based interpolation model with a kernelized matrix completion framework, with the impact weights provided by neighbors computed in a new Hilbert space containing more features. In our model, the kernel function is combined with a similarity measurement to achieve better approximation for unknown ratings. Furthermore, we extend our model with a non-linear multi-kernel framework which learns weights automatically to improve the model. Finally, we conduct extensive experiments on several real-world datasets. The outcomes show that the proposed methods work effectively and improve the performance of the rating prediction task compared to both the traditional and state-of-the-art approaches.

Research Area(s)

  • Kernel learning, Matrix completion, Matrix interpolation, Multi-kernel learning, Recommender systems