关键词: 多样性, 用户偏好, 用户满意度, 相似度

Abstract:

Traditional recommendation algorithms mainly focus on the recommendation accuracy, while user different preferences for items and diversity needs also affect user experience and satisfaction. Aiming at the problem, a new algorithm is proposed. Firstly, to improve the accuracy of the similarity between items, the algorithm combines the rating differences between different items by users when computing the similarity. Secondly, according to user historical rating data and item category data, a user-category weight matrix is concluded. On the one hand, a definition of diversity based on entropy depends on the matrix. In addition, according to the calculation formula of the user’s interest in the item, an initial recommendation sequence in descending order is generated,a recommendation based on user diversity preference can be implemented by combining a setting of error threshold for user preference and the user-category weight matrix, N recommended items are finally generated, which aims at improving user satisfaction on the premise of ensuring accuracy and diversity. Experiments on the movielens datasets with different versions show that the proposed algorithm can effectively improve the recommendation accuracy and user satisfaction compared with several classic algorithms.

Key words: diversity, user preference, user satisfaction, similarity