Non-IID Learning for Recommendation, Time Series and Hashing

Zhang, Qi

Non-IID Learning for Recommendation, Time Series and Hashing

Zhang, Qi

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Publication Type:: Thesis
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Zhang, Qi
dc.date.accessioned	2023-09-06T04:36:31Z
dc.date.available	2023-09-06T04:36:31Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10453/171956
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	For several decades, the independent and identically distributed (short for IID) assumption has laid the foundation of data learning, simplifying real-world data's intricate nature for effectively achieving approximate, traceability, and asymptotic problem-solving. Unfortunately, real-world scenarios generally go beyond the IID assumption and count on specific knowledge and capability to address practical problems and challenges, where IID may show significant limitations and gaps. A broad-reaching non-IID thinking is to explore and exploit the intrinsic heterogeneities and couplings of real-world data, which has been increasingly attractive and prevalent in data learning research and applications. However, non-IIDness shows diversified properties with different data scenarios, for example, heterogeneities in data types, attributes, sources, and couplings within and between structures, distributions, and variables. It is far from reaching a unified non-IID learning paradigm for addressing various real-world heterogeneities and couplings. More importantly, it is also extremely challenging to exhaustively tailor non-IID data learning methodologies for specified scenarios and applications. In this thesis, I explore non-IID learning in terms of different applications, specifically recommender systems, multivariate time series (MTS) analysis, and learning to hash, to enlighten non-IID methodologies and techniques. The elaborately chosen applications and scenarios penetrate our daily living, studying, working, and entertaining activities, and cover various tasks of classification, ranking, representation, and retrieval. Accordingly, the main research objectives include modeling and learning the non-IIDness in recommender systems, multivariate time series (MTS) analysis, and learning to hash, respectively, and delivering non-IID models to effectively handle the scenarios with both IID and non-IID data.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/171956/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2023 Qi Zhang
dc.rights	au.edu.uts.lib/cph
dc.title	Non-IID Learning for Recommendation, Time Series and Hashing	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

For several decades, the independent and identically distributed (short for IID) assumption has laid the foundation of data learning, simplifying real-world data's intricate nature for effectively achieving approximate, traceability, and asymptotic problem-solving. Unfortunately, real-world scenarios generally go beyond the IID assumption and count on specific knowledge and capability to address practical problems and challenges, where IID may show significant limitations and gaps. A broad-reaching non-IID thinking is to explore and exploit the intrinsic heterogeneities and couplings of real-world data, which has been increasingly attractive and prevalent in data learning research and applications. However, non-IIDness shows diversified properties with different data scenarios, for example, heterogeneities in data types, attributes, sources, and couplings within and between structures, distributions, and variables. It is far from reaching a unified non-IID learning paradigm for addressing various real-world heterogeneities and couplings. More importantly, it is also extremely challenging to exhaustively tailor non-IID data learning methodologies for specified scenarios and applications. In this thesis, I explore non-IID learning in terms of different applications, specifically recommender systems, multivariate time series (MTS) analysis, and learning to hash, to enlighten non-IID methodologies and techniques. The elaborately chosen applications and scenarios penetrate our daily living, studying, working, and entertaining activities, and cover various tasks of classification, ranking, representation, and retrieval. Accordingly, the main research objectives include modeling and learning the non-IIDness in recommender systems, multivariate time series (MTS) analysis, and learning to hash, respectively, and delivering non-IID models to effectively handle the scenarios with both IID and non-IID data.

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http://hdl.handle.net/10453/171956