Predicting user-item links in recommender systems based on similarity-network resource allocation

Recommender systems and link prediction techniques have been widely used in areas such as online information filtering and improving user retrieval efficiency, and their performance and principles are of significant research interest. However, existing mainstream recommendation algorithms still face many challenges, such as the contradiction between prediction accuracy and recommendation diversity, and the limited scalability of algorithms due to the need to use a large number of neighbors for prediction. To address these two issues, this paper designs a user-item link prediction algorithm based on resource allocation within the user similarity network to enhance prediction accuracy while maintaining recommendation diversity and using as few neighbors as possible to achieve better algorithm scalability. We first calculate inter-user similarity based on user history ratings and construct a similarity network among users by filtering the similarity results; subsequently, based on the centrality and community features in this network, we design a similarity measure for resource allocation that incorporates the bipartite graph model and the similarity network; finally, we use this similarity method to select the set of prediction target neighbors, synthesize and use the similarity results, centrality, and community features for the prediction of user-item links. Experimental results on two well-known datasets with three state-of-the-art algorithms show that the proposed approach can improve the prediction accuracy by 2.34% to 15.76% in a shorter time and maintain a high recommendation diversity, and the ranking accuracy of recommendation is also improved. Compared with the benchmark algorithm with the second highest performance ranking, the method designed in this paper can further reduce the number of neighbors required at optimal prediction error by 25% to 56%. The study reveals that resource allocation in similarity networks successfully mines the features embedded in the recommender system, laying the foundation for further understanding the recommender system and improving the performance of related prediction methods.