A framework for improving find best marketing targets using a hybrid genetic algorithm and neural networks

Neysiani, Behzad Soleimani; Soltani, Nasim; Ghezelbash, Shima

doi:10.1109/kbei.2015.7436136

Cited by 5 publications

(1 citation statement)

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“…In [23], researchers show improving the accuracy of collaborative filtering recommendations with clustering and ARM. In this research, they preprocessed data to discover similar pattern among users and after that used cluster techniques to reduce the size of data.…”

Section: Related Workmentioning

confidence: 99%

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

Neysiani¹,

Soltani²,

Mofidi³

et al. 2019

IJITCS

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Recommender systems that possess adequate information about users and analyze their information, are capable of offering appropriate items to customers. Collaborative filtering method is one of the popular recommender system approaches that produces the best suggestions by identifying similar users or items based on their previous transactions. The low accuracy of suggestions is one of the major concerns in the collaborative filtering method. Several methods have been introduced to enhance the accuracy of this method through the discovering association rules and using evolutionary algorithms such as particle swarm optimization. However, their runtime performance does not satisfy this need, thus this article proposes an efficient method of producing cred associations rules with higher performances based on a genetic algorithm. Evaluations were performed on the data set of MovieLens. The parameters of the assessment are: run time, the average of quality rules, recall, precision, accuracy and F1-measurement. The experimental evaluation of a system based on our algorithm outperforms show than the performance of the multiobjective particle swarm optimization association rule mining algorithm, finally runtime has dropped by around 10%.

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Section: Related Workmentioning

confidence: 99%