RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender s...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-04, Vol.2022, p.8714870-15
Main Authors: Nia, Raheleh Ghouchan Nezhad Noor, Jalali, Mehrdad
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Clustering
Cold
Cold starts
Collaboration
Decision making
Evolutionary algorithms
Genetic algorithms
Mental task performance
Optimization
Ratings & rankings
Recommender systems
Social networks
System effectiveness
Trust
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Summary:Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/8714870