On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs

We study a matrix completion problem which leverages a hierarchical structure of social similarity graphs as side information in the context of recommender systems. We assume that users are categorized into clusters, each of which comprises sub-clusters (or what we call "groups"). We consi...

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Veröffentlicht in:	IEEE transactions on information theory Jg. 70; H. 3; S. 2039 - 2075
Hauptverfasser:	Ahn, Junhyung, Elmahdy, Adel, Mohajer, Soheil, Suh, Changho
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Clustering algorithms Clusters Collaborative filtering Complexity Complexity theory Filtering graph side information Graphs Information theory Lower bounds matrix completion problem Recommender systems Similarity Sparse matrices
ISSN:	0018-9448, 1557-9654
Online-Zugang:	Volltext
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