Hybrid crow search and uniform crossover algorithm-based clustering for top- N recommendation system
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ORIGINAL ARTICLE
Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system Walaa H. El-Ashmawi1,2 • Ahmed F. Ali1 • Adam Slowik3 Received: 3 July 2019 / Accepted: 27 October 2020 The Author(s) 2020
Abstract Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top-N recommendations are presented for the corresponding user according to the most feasible cluster’s members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other metaheuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function. Keywords Crow search algorithm Uniform crossover Recommendation system User-based collaborative filtering
1 Introduction Currently, there are more than four billion internet users all over the world who have access to more than one billion websites [1]. Due to the huge amount of information & Adam Slowik [email protected] Walaa H. El-Ashmawi [email protected]; [email protected] Ahmed F. Ali [email protected] 1
Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
2
Faculty of Computer Science, Misr International University, Cairo, Egypt
3
Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland
available, finding relevant information on the internet is an important issue. Among solutions that cope with this issue is a recommender system (RS). RS can be considered as an information filtering tool or support in the decision-making process that recommends items to users or filters and sorts information. Currently, recommendation algorithms have been widely used in Spotify, Facebook, TripAdvisor, and many others. The RSs are categorized
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