基于加权信息熵相似性的协同过滤算法-《郑州大学学报(工学版)》

文章信息/Info

Title:: Collaborative Filtering Algorithm Based on Weighted Information Entropy Similarity

作者:: 刘文龙; 张桂芸; 陈喆; 等.; 天津师范大学计算机与信息工程学院,天津,300387, 天津师范大学城市与环境科学学院,天津,300387

Author(s):: LIU Wenlong; ZHANG Guiyun; CHEN Zhe; 1.College of Computer and Information Engineering, Tianjin Normal University ,Tianjin 300387 ,China;2. College of Urban andEnvironmental Seience,Tianjin Normal University ,Tianjin 300387,China

Keywords:: weighted information entropy ; similarity ealculation; collaborative filtering; personalized recommendation

摘要:: 协同过滤算法是推荐系统中最为成功的技术之一,相似性计算是协同过滤算法的核心.针对传统的相似度计算方法在数据稀疏的情况下推荐不准确问题,提出了基于项目间差异信息熵的相似度计算方法,先通过差异值和共同评价数目对信息熵进行加权,再归一化处理来计算项目间的相似度.用基于项目( Item - based)相似性的协同过滤算法进行了实验验证,实验结果表明,该算法提高了个性化推荐精度.

Abstract:: Collaborative filtering algorithm is one of the most successful recommender system technology. Thesimilarity calculation is the core of the collaborative filtering algorithm, In view of the poor predication qualityexisting in traditional similarity calculation with sparse data, we propose a similarity calculation method basecon the information entropy between differences of items. First , we weight the entropy by the difference and com.mon evaluation and then normalized it to measure the similarity between items, Verified by experiments with i.tem-based collaborative filtering algorithm, the results show that it improves accuracy of personalized recommendation.