Co-Embedding: Discovering Communities on Bipartite Graphs through Projection - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2021

Co-Embedding: Discovering Communities on Bipartite Graphs through Projection

Résumé

Many datasets take the form of a bipartite graph where two types of nodes are connected by relationships, like the movies watched by a user or the tags associated with a file. The partitioning of the bipartite graph could be used to fasten recommender systems, or reduce the information retrieval system's index size, by identifying groups of items with similar properties. This type of graph is often processed by algorithms using the Vector Space Model representation, where a binary vector represents an item with 0 and 1. The main problem with this representation is the dimension relatedness, like words' synonymity, which is not considered. This article proposes a co-clustering algorithm using items projection, allowing the measurement of features similarity. We evaluated our algorithm on a cluster retrieval task. Over various datasets, our algorithm produced well balanced clusters with coherent items in, leading to high retrieval scores on this task..
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Dates et versions

hal-03934037 , version 1 (11-01-2023)

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  • HAL Id : hal-03934037 , version 1

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Gaëlle Candel, David Naccache. Co-Embedding: Discovering Communities on Bipartite Graphs through Projection. FICC 2022, Jan 2021, San Francisco, United States. ⟨hal-03934037⟩
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