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ICDM
2006
IEEE

An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task

13 years 9 months ago
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...
François Fouss, Luh Yen, Alain Pirotte, Mar
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICDM
Authors François Fouss, Luh Yen, Alain Pirotte, Marco Saerens
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