An experimental investigation of graph kernels on a collaborative recommendation task

  • Fouss F
  • Yen L
  • Pirotte A
 et al. 
  • 50


    Mendeley users who have this article in their library.
  • 53


    Citations of this article.


This work presents a systematic comparison between seven kernels (or similarity matrices) on a graph, namely the exponential diffusion kernel, the Laplacian diffusion kernel, the von Neumann kernel, the regularized Laplacian kernel, the commute time kernel, and finally the Markov diffusion kernel and the cross-entropy diffusion matrix - both introduced in this paper - on a collaborative recommendation task involving a database. The database is viewed as a graph where elements are represented as nodes and relations as links between nodes. From this graph, seven kernels are computed, leading to a set of meaningful proximity measures between nodes, allowing to answer questions about the structure of the graph under investigation; in particular, recommend items to users. Cross- validation results indicate that a simple nearest-neighbours rule based on the similarity measure provided by the regularized Laplacian, the Markov diffusion and the commute time kernels performs best. We therefore recommend the use of the commute time kernel for computing similarities between elements of a database, for two reasons: (1) it has a nice appealing interpretation in terms of random walks and (2) no parameter needs to be adjusted.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text


  • Francois Fouss

  • Luh Yen

  • Alain Pirotte

  • Marco Saerens

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free