Name
Title | ||
|---|---|---|
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task |
Abstract | ||
|---|---|---|
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. Crossvalidation 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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICDM.2006.18 | ICDM |
Keywords | Field | DocType |
cross-entropy diffusion matrix,markov diffusion,commute time kernel,graph kernels,regularized laplacian,experimental investigation,similarity matrix,markov diffusion kernel,exponential diffusion kernel,von neumann kernel,laplacian diffusion kernel,collaborative recommendation task,regularized laplacian kernel,rule based,exponential distribution,cross entropy,data mining,markov processes,random walk,graph theory,cross validation | Graph kernel,Laplacian matrix,Data mining,Kernel smoother,Radial basis function kernel,Kernel embedding of distributions,Computer science,Tree kernel,Artificial intelligence,String kernel,Variable kernel density estimation,Machine learning | Conference |
ISSN | ISBN | Citations |
1550-4786 | 0-7695-2701-9 | 51 |
PageRank | References | Authors |
2.58 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francois Fouss | 1 | 624 | 34.41 |
| Luh Yen | 2 | 383 | 28.82 |
| Alain Pirotte | 3 | 916 | 260.52 |
| Marco Saerens | 4 | 1221 | 87.07 |