Influence based cost optimization on user preference

Yang, J; Zhang, Y; Zhang, W; Lin, X

Influence based cost optimization on user preference

Yang, J Zhang, Y

Zhang, W Lin, X

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, 2016, pp. 709 - 720
Issue Date:: 2016-06-22

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Field	Value	Language
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Lin, X	en_US
dc.date.issued	2016-06-22	en_US
dc.identifier.citation	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, 2016, pp. 709 - 720	en_US
dc.identifier.isbn	9781509020195	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122809
dc.description.abstract	© 2016 IEEE. The popularity of e-business and preference learning techniques have contributed a huge amount of product and user preference data. Analyzing the influence of an existing or new product among the users is critical to unlock the great scientific and social-economic value of these data. In this paper, we advocate the problem of influence-based cost optimization for the user preference and product data, which is fundamental in many real applications such as marketing and advertising. Generally, we aim to find a cost optimal position for a new product such that it can attract at least k or a particular percentage of users for the given user preference functions and competitors' products. Although we show the solution space of our problem can be reduced to a finite number of possible positions (points) by utilizing the classical k-level computation techniques, the computation cost is still very expensive due to the nature of the high combinatorial complexity of the k-level problem. To alleviate this issue, we develop efficient pruning and query processing techniques to significantly improve the performance. In particular, our traverse-based 2-dimensional algorithm is very efficient with time complexity O(n) where n is the number of user preference functions. For general multi-dimensional spaces, we develop space partition based algorithm to significantly improve the performance by utilizing cost-based, influence-based and local dominance based pruning techniques. Then, we show that the performance of the partition based algorithm can be further enhanced by utilizing sampling approach, where the problem can be reduced to the classical half-space intersection problem. We demonstrate the efficiency of our techniques with extensive experiments over real and synthetic datasets.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE140100679
dc.relation	http://purl.org/au-research/grants/arc/DP130103245
dc.relation.ispartof	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016	en_US
dc.relation.isbasedon	10.1109/ICDE.2016.7498283	en_US
dc.title	Influence based cost optimization on user preference	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. The popularity of e-business and preference learning techniques have contributed a huge amount of product and user preference data. Analyzing the influence of an existing or new product among the users is critical to unlock the great scientific and social-economic value of these data. In this paper, we advocate the problem of influence-based cost optimization for the user preference and product data, which is fundamental in many real applications such as marketing and advertising. Generally, we aim to find a cost optimal position for a new product such that it can attract at least k or a particular percentage of users for the given user preference functions and competitors' products. Although we show the solution space of our problem can be reduced to a finite number of possible positions (points) by utilizing the classical k-level computation techniques, the computation cost is still very expensive due to the nature of the high combinatorial complexity of the k-level problem. To alleviate this issue, we develop efficient pruning and query processing techniques to significantly improve the performance. In particular, our traverse-based 2-dimensional algorithm is very efficient with time complexity O(n) where n is the number of user preference functions. For general multi-dimensional spaces, we develop space partition based algorithm to significantly improve the performance by utilizing cost-based, influence-based and local dominance based pruning techniques. Then, we show that the performance of the partition based algorithm can be further enhanced by utilizing sampling approach, where the problem can be reduced to the classical half-space intersection problem. We demonstrate the efficiency of our techniques with extensive experiments over real and synthetic datasets.

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http://hdl.handle.net/10453/122809