A Lagrangian Approach for Multiple Personalized Campaigns

The multicampaign assignment problem is a campaign model to overcome the multiple-recommendation problem that occurs when conducting several personalized campaigns simultaneously. In this paper, we propose a Lagrangian method for the problem. The original problem space is transformed to another simp...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 20; číslo 3; s. 383 - 396
Hlavní autori:	Yong-Hyuk Kim, Yoon, Y., Byung-Ro Moon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.03.2008 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithm design and analysis Algorithms for data and knowledge management Applied sciences Computer science; control theory; systems Constrained optimization Constraint optimization Customer satisfaction Customers Data processing. List processing. Character string processing Dynamic programming Electronic Commerce Evolutionary Evolutionary computing and genetic algorithms Exact sciences and technology Genetic algorithms Heuristic algorithms Lagrange multipliers Lagrangian functions Large-scale systems Marketing Mathematical analysis Mathematical models Memory organisation. Data processing Moon Operations research Optimization Personalized Software Studies Upper bound Vectors (mathematics) Algorithm design and analysis Electronic Commerce Evolutionary computing and genetic algorithms Algorithms for data and knowledge management Marketing Constrained optimization Lagrangian Evolutionary algorithm Customization Linear time multicampaign assignment Modeling Recommendation Lagrange multiplier Genetic algorithm Optimal solution Lagrangian method Personalized campaign
ISSN:	1041-4347, 1558-2191
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Popis
Shrnutí:	The multicampaign assignment problem is a campaign model to overcome the multiple-recommendation problem that occurs when conducting several personalized campaigns simultaneously. In this paper, we propose a Lagrangian method for the problem. The original problem space is transformed to another simpler one by introducing Lagrange multipliers, which relax the constraints of the multicampaign assignment problem. When the Lagrangian vector is supplied, we can compute the optimal solution under this new environment in O( NK2 ) time, where N and K are the numbers of customers and campaigns, respectively. This is a linear-time method when the number of campaigns is constant. However, it is not easy to find a Lagrangian vector in exact accord with the given problem constraints. We thus combine the Lagrangian method with a genetic algorithm to find good near-feasible solutions. We verify the effectiveness of our evolutionary Lagrangian approach in both theoretical and experimental viewpoints. The suggested Lagrangian approach is practically attractive for large-scale real-world problems.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	1041-4347 1558-2191
DOI:	10.1109/TKDE.2007.190701