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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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 20; no. 3; pp. 383 - 396
Main Authors: Yong-Hyuk Kim, Yoon, Y., Byung-Ro Moon
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.2008
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2007.190701