计算机科学与探索2018，Vol.12Issue(2)：197-207,11.DOI:10.3778/j.issn.1673-9418.1611020
联合用户兴趣矩阵及全局偏好的推荐算法
Recommendation Algorithm with User's Interest Matrix and Global Preference
摘要
Abstract
How to recommend the most interested information to users accurately from a large amount of disordered information has become an important research subject in service recommendation system.This paper propose a recommendation algorithm based on the combination of the user's interest matrix and the global preferences,which is used for personalized service recommendation.This paper firstly introduces the interest tag mechanism to form the user interest chain,and to form the user interest matrix by filling unevaluated service and complementing evaluated service on the user service rating set.Then,this paper calculates the partial similarity by the Euclidean distance of the user's interest matrix.Lastly,this paper combines the global preference similarity based on the user cognitive difference and the global behavior difference.The algorithm can effectively integrate the user's preference information,also reduce the sparsity of the data set,and improve the recommendation accuracy.A large number of experiments on the real MovieLens 1M data set show that the proposed algorithm significantly improves the recommendation accuracy compared with the current representative recommendation algorithms.关键词
协同过滤/兴趣链/兴趣矩阵/全局偏好/相似度Key words
collaborative filtering/interest chain/interest matrix/global preference/similarity分类
信息技术与安全科学引用本文复制引用
张以文,艾晓飞,崔光明,钱付兰..联合用户兴趣矩阵及全局偏好的推荐算法[J].计算机科学与探索,2018,12(2):197-207,11.基金项目
The National Natural Science Foundation of China under Grant No.61175046(国家自然科学基金) （国家自然科学基金）
the National Key Technology R&D Program of China under Grant No.2015BAK24B01(国家科技支撑计划) （国家科技支撑计划）
the Key Project of Nature Science Research for Universities of Anhui Province under Grant No.KJ2016A03(安徽省高校自然科学基金重点项目). （安徽省高校自然科学基金重点项目）
