A Many-objective Hybrid Recommendation Algorithm Based on User Grouping

The initial recommendation list in the multi-objective recommendation algorithm is generated by a specific algorithm, meaning the accuracy of the multi-objective algorithm is constrained by the performance of the algorithm used to generate the initial recommendations. To achieve better accuracy, the many-objective hybrid recommendation algorithm based on user grouping algorithm is proposed. The algorithm constructs a dual preference model, and employs a many-objective optimization approach to balance conflicts among these objectives. However, as the number of users increases, the population dimension of the many-objective hybrid recommendation algorithm also grows, resulting in challenges related to convergence. To mitigate this issue, users are grouped to reduce the population dimension. Frequency-based genes are derived from user similarity for crossover operations, generating offspring more efficiently. Experimental results demonstrate that user grouping significantly reduces computational cost and enhances the algorithm’s performance.