A many-objective optimization recommendation algorithm based on knowledge mining

Abstract Recommendation system (RS) is a technology that provides accurate recommendation for users. In order to make the recommendation results more accurate and diverse, we proposed a rating-based many-objective hybrid recommendation model that can optimize the accuracy, recall, diversity, novelty and coverage of the recommendation simultaneously. Additionally, a new generation-based fitness evaluation strategy and a partition-based knowledge mining strategy are proposed to improve the many-objective evolutionary algorithms (MaOEAs) to enhance the performance of the recommendations generated by the model. Finally, comparing the proposed many-objective optimization recommendation algorithm with the existing standard MaOEAs, experimental results demonstrate that the proposed algorithm can provide recommendations with the more and novel items on the basis of accuracy and diversity for users.

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