NDLI: An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task

Content Provider	IEEE Xplore Digital Library
Author	Fouss, F. Luh Yen Pirotte, A. Saerens, M.
Copyright Year	2006
Description	Author affiliation: Inf. Syst. Res. Unit, Univ. Catholique de Louvain, Louvain-la-Neuve (Fouss, F.; Luh Yen; Pirotte, A.; Saerens, M.)
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. 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.
Starting Page	863
Ending Page	868
File Size	218884
Page Count	6
File Format	PDF
ISBN	0769527017
ISSN	15504786
DOI	10.1109/ICDM.2006.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Collaboration Collaborative work Laplace equations Relational databases Data mining Joining processes Information systems Particle measurements Time measurement
Content Type	Text
Resource Type	Article

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