Optimization based design with subgradient method in recommender system

摘要

Recommender systems are becoming increasingly important to propose personalized recommendations for individual users and businesses. In the literature, the proposed recommender systems algorithms focus on improving the accuracy of the recommendation, other important factors affecting the quality of the recommendation are usually overlooked, such as the diversity of recommendation list that presented to the user. In this study, a recommender system algorithm was developed to generate more diverse recommendations and to calculate the accuracy of the recommendation with different comparison techniques, so it is aimed to present a recommendation list to the user's with the balance of recommendation accuracy-diversity. We studied on the currently well-used real data sets and recommendation algorithms that use different optimization techniques, it has been observed that the diversity of recommendation has consistently increased the gain in system accuracy.