An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task

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 theref...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: François Fouss, Luh Yen, Alain Pirotte, Marco Saerens
• Keywords: Commute Time Kernel, Data Mining, Diffusion Kernel, ICDM 2006, Laplacian Diffusion Kernel