摘要
为解决传统的协同过滤算法不能准确理解用户的喜好,影响推荐准确率和推荐效果,提出基于社会化标签语义相似度的协同过滤算法.算法以标签语义相似度为基础,将项目资源和相关标签的语义信息纳入,显著提高了推荐系统的预测性能.研究结果表明:与以具体评分数据为基础的算法相比,该算法较好地解决了词相似度和句子相似度计算问题,推荐准确度和性能较以往的协同过滤算法有明显提高,改善了推荐效果.
In order to solve the traditional collaborative filtering algorithm can not accurately understand the user＇s pref- erences, affect the recommendation accuracy and recommendation effect, a collaborative filtering algorithm based on social tags semantic similarity is proposed. Based on the semantic similarity of tags, the semantic information of project re- sources and related tags is included, and the prediction performance of the recommendation system is significantly im- proved. Research results show that： compared with the algorithm based on the user rating, the proposed algorithm can solve the problem of word similarity and sentence similarity computation, and the recommendation accuracy and recom- mendation effect, as well as the performance of the proposed algorithm is significantly improved compared with the previ- ous collaborative filtering algorithm.
出处
《华侨大学学报（自然科学版）》
CAS
北大核心
2016年第1期84-87,共4页
Journal of Huaqiao University(Natural Science)
基金
广东省高等学校学科与专业建设专项(2013LYM_0110)
广东省教育科学规划专项(14JXN060)
关键词
协同过滤
推荐系统
社会化标签
语义相似度
预测性能
collaborative filtering
recommendation system
social tags
semantic similarity
prediction performance
