Diversity Recommendation Algorithm Based on User Coverage and Rating Differences - CHEN Zhuang, ZOU Hai-tao, ZHENG Shang, YU Hua-long, GAO Shang (Jisuanji kexue, 2022)
Traditional recommender systems usually focus on improving recommendation accuracy while neglecting the diversity of recommendation lists.However,several studies have shown that,users’ diversity needs also take an important part of their sa-tisfaction.In this paper,a user-coverage model based on item rating differences is proposed.During generating user’s interest domain(user coverage),on the one hand,the model combines rating differences between users across an item with user-coverage model effectively,thus obtaining a more precise interest domain of the user.On the other hand,objective function is constructed in the form of vector by mapping a user’s and an itemset’s interest domain to two m-dimensional vectors (called user vector and itemset vector respectively),which can reduce the number of iterations in the calculation process.In addition,a new items selection strategy is provided by similarity relationship between those two m-dimensional vectors.The proposed model has superior performance in both accuracy and diversity.User vector for a specific user is a constant,however,finding the most matching itemset vector will be an NP-hard problem.During the implementation of the proposed model,a greedy algorithm is chosen to solve the optimization problem based on critical theoretical boundary.Experimental comparisons with the state-of-the-arts related to diversity recommendation in recent years on two real-world data sets demonstrate that the proposed algorithm can effectively improve the diversity of the recommendation.