《计算机应用研究》|Application Research of Computers

基于模糊聚类和改进混合蛙跳的协同过滤推荐

Collaborative filtering recommendation based on fuzzy clustering and improved shuffled frog leaping algorithm

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作者 许智宏,田雨,闫文杰,暴利花
机构 1.河北工业大学 计算机科学与软件学院,天津 300401;2.河北省大数据计算重点实验室,天津 300401
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文章编号 1001-3695(2018)10-2908-04
DOI 10.3969/j.issn.1001-3695.2018.10.006
摘要 由于传统的协同过滤推荐算法存在很多缺陷,如数据稀疏性、冷启动、低推荐精度等,提出了一种基于模糊聚类和改进混合蛙跳的协同过滤推荐算法。首先利用一种构造的基于时间的指数遗忘函数对原始评分数据进行处理;然后根据得到的基于时间衰退的评分矩阵对用户进行模糊C-均值(FCM)聚类,并找出与目标用户有较高相似性的前几个类作为候选邻居集;再用改进的混合蛙跳算法找到最近邻居集;最后求出目标用户对未参与项目的预测评分。经实验证明,该算法比其他一些算法的推荐精度要高,且由于数据稀疏性引起的不良影响也得到了有效的缓解。
关键词 协同过滤推荐;指数遗忘函数;模糊C-均值聚类;混合蛙跳算法
基金项目 河北省自然科学基金资助项目(F2015202214)
河北省科技计划资助项目(15210506)
天津市自然科学基金资助项目(16JCQNJC00400)
本文URL http://www.arocmag.com/article/01-2018-10-006.html
英文标题 Collaborative filtering recommendation based on fuzzy clustering and improved shuffled frog leaping algorithm
作者英文名 Xu Zhihong, Tian Yu, Yan Wenjie, Bao Lihua
机构英文名 1.SchoolofComputerScience&Engineering,HebeiUniversityofTechnology,Tianjin300401,China;2.HebeiProvinceKeyLaboratoryofBigDataCalculation,Tianjin300401,China
英文摘要 As the traditional collaborative filtering recommendation algorithm existed many defects, such as data sparseness, cold start and low recommendation accuracy, this paper proposed a collaborative filtering recommendation algorithm based on fuzzy clustering and improved shuffled frog leaping algorithm. The algorithm first used the constructed time-based exponential forgetting function to process the original score. Then, it clustered the users with fuzzy C-means (FCM) clustering according to the obtained scoring matrix based on time lag, and found the first few classes with higher similarity to the target user as candidate neighbor sets. And then it used the improved shuffled frog leaping algorithm to find the nearest neighbor sets. Finally, it calculated the prediction score of the target user was not involved in the project. Experiments show that the proposed algorithm is more accurate than some other algorithms, and effectively alleviate the adverse effects due to data sparseness.
英文关键词 collaborative filtering recommendation; exponential forgetting function; fuzzy C-means clustering; shuffled frog leaping algorithm
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收稿日期 2017/5/26
修回日期 2017/7/16
页码 2908-2911
中图分类号 TP301.6
文献标志码 A