Optimization and Prediction of Users' favorite Programs Using Collaborative Filtering Approach and Cuckoo Algorithm
Subject Areas : مهندسی برق و کامپیوترR. Molaee Fard 1 * , J. Mohammadzadeh 2 , payam yarahmadi 3
1 - Dept. of Comp. Eng., Faculty of Comp. and Inf. Tech., Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Dept. of Comp. Eng., Karaj Branch, Islamic Azad University, Karaj, Iran
3 - Dept. of Comp. Eng., Taras National University of Kyiv, Kyiv, Ukraine
Keywords: Recommender system, mobile application, collaborative filtering, cuckoo algorithm, data mining.,
Abstract :
This research presents a method to improve mobile application recommendation systems using collaborative filtering and the cuckoo meta-heuristic algorithm. The SW-DBSCAN algorithm was used for data clustering. This algorithm was able to achieve an efficiency of 99% in the clustering section, which was better than other similar algorithms. Also, the cuckoo algorithm was used to optimize the data, which achieved a performance of 98% and achieved better performance than the firefly, gray wolf, and particle optimization algorithms. For the prediction part, a neural network algorithm was used, which was also able to achieve acceptable performance compared to other similar algorithms. Ultimately, this information is provided to the user using a recommender system based on collaborative filtering. One of the problems that recommender systems face is the problem of having the same meaning. The problem of having the same meaning occurs when an item is represented by two or more names, names that have the same meaning. In such cases, the recommender system cannot distinguish whether these names represent different items or all refer to the same item, which is what this method attempts to solve. Also, according to research, using this recommended method can correctly identify user needs up to 94% of the time and offer appropriate suggestions to the user.
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