Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation

Tang, Xiaoyu (2016) Multidimensional recommendation framework based on the incorporation of nearest neighbourhood and tensor factorisation. PhD thesis, Queensland University of Technology.


This thesis investigated in depth the distinctive relations between users, items, and tags in recommender systems. This thesis proposed a number of novel techniques to find users’ interests and improved the accuracy of recommendations in collaborative filtering recommender systems. The methods proposed in this thesis examined the integration of neighbourhood approaches and factorisation models for user interest modelling and item recommendation.

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14 since deposited on 12 Jul 2016
14 in the past twelve months

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ID Code: 96195
Item Type: QUT Thesis (PhD)
Supervisor: Xu, Yue & Geva, Shlomo
Keywords: Collaborative Filtering, User Profiling, Recommender Systems, Multidimensional datasets, Folksonomy, Tags, Taxonomy, Personalisation
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 12 Jul 2016 01:24
Last Modified: 12 Jul 2016 01:24

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