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 meta-heuristic 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.
Highlights
There are more than four billion internet users all over the world who have access to more than one billion websites [1]
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
In the case of the number of users grouped to 5 clusters for better recommendations, the hybrid crow search and uniform crossover algorithm (HCSUC) has better Mean Absolute Error (MAE) results up to 8% with top-8 recommendations when compared with particle swarm optimization (PSO), up to 7% with top-4 and top-12 recommendations when compared with African buffalo optimization (ABO) and 3% when compared with standard crow search algorithm (CSA) for different topN recommendations
Summary
There are more than four billion internet users all over the world who have access to more than one billion websites [1]. Hybrid filtering combines more than one existing filtering technique [14, 15] to overcome the limitation issues of RS, while knowledge-base filtering uses a relationship and inference about the user needs and preferences in the recommendation. We follow the user-based collaborative filtering recommendation model. The work in this paper has tried to utilize one of the recent meta-heuristic algorithms for clustering technique in user-based collaborative filtering. The proposed algorithm is called hybrid crow search and uniform crossover algorithm (HCSUC). Invoking the uniform crossover in the HCSUC algorithm can increase the diversity of search which can help the proposed algorithm to escape from trapping in local minima Such a hybrid algorithm can achieve an improved clustering solution for a practical clustering-based recommender system.
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