Algorithms for clustering data
Algorithms for clustering data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
One-to-one marketing on the internet
ICIS '99 Proceedings of the 20th international conference on Information Systems
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Dynamic Programming
Multicampaign Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
Knowledge and Information Systems
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
A Lagrangian Approach for Multiple Personalized Campaigns
IEEE Transactions on Knowledge and Data Engineering
Guest Editorial Special Issue on Particle Swarm Optimization
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
New theoretical findings in multiple personalized recommendations
Proceedings of the 2010 ACM Symposium on Applied Computing
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This paper presents a novel swarm intelligence approach to optimize simultaneously multiple campaigns assignment problem, which is a kind of searching problem aiming to find out a customer-campaign matrix to maximize the outcome of multiple campaigns under certain restrictions. It is treated as a very challenging problem in marketing. In personalized marketing it is very important to optimize the customer satisfaction and targeting efficiency. Particle swarm optimization (PSO) method can be chosen as a suitable tool to overcome the multiple recommendation problems that occur when several personalized campaigns conducting simultaneously. Compared with original PSO we have modified the particle representation and velocity by a multi-dimensional matrix, which represents the customer-campaign assignment. A new operator known as REPAIRED is introduced to restrict the particle within the domain of solution space. The proposed operator helps the particle to fly into the better solution areas more quickly and discover the near optimal solution. We measure the effectiveness of the propose method with two other methods know as Random and Independent using randomly created customer-campaign preference matrix. Further a generalized Gaussian response suppression function is introduced and it differs among customer classes. An extensive simulation studies are carried out varying on the small to large scale of the customer-campaign assignment matrix and the percentage of recommendations. Simulation result shows a clear edge between PSO and other two methods.