计算机科学 ›› 2014, Vol. 41 ›› Issue (3): 36-40.
张佳乐,梁吉业,庞继芳,王宝丽
ZHANG Jia-le,LIANG Ji-ye,PANG Ji-fang and WANG Bao-li
摘要: 使用关联规则推荐工具会遇到最优推荐规则选取难、规则信息不能充分利用等问题。利用较易获取的应用领域知识可有效解决这类问题。针对仅有商品名称和评分信息的推荐情形,提出一种基于行为和评分相似性的关联规则群推荐算法,该算法将规则及相应的评分信息视为推荐专家,将推荐结论相同的专家合并为一个专家组,利用客户行为和评分的双重相似性计算专家权重,并利用群决策的思想集结专家组的推荐意见,从而给出最佳推荐方案。最后通过实例和实验说明了该算法的可行性和有效性。
[1] Park D H,Kim H K.A literature review and classification ofrecommender systems research[J].Expert Systems with Applications,2012,39(11):10059-10072 [2] 哈进兵,郑锐,甘利人.一种基于加权关联规则的协同推荐算法[J].情报学报,2010,9(4):718-722 [3] 龙舜,蔡跳,林佳雄.一个基于演化关联规则挖掘的个性化推荐模型[J].暨南大学学报,2012,3(3):264-267 [4] Thabtah F,Cowling P,Peng Y.MMAC:A new multi-class,multi-label associative classification approach[C]∥ICDM 2004:Proceedings of the 4th IEEE International Conference on Data Mining.Brighton,UK,2004:217-224 [5] 周欣,沙朝锋,朱扬勇.兴趣度-关联规则的又一个阈值[J].计算机研究与发展,2000,7(5):627-633 [6] 肖波.可信关联规则挖掘算法研究[D].北京:北京邮电大学,2009 [7] 李广原,杨炳儒,周如旗.一种基于约束的关联规则挖掘算法[J].计算机科学,2012,9(1):244-247 [8] 杨红菊,梁吉业.一种有效的关联规则的挖掘方法[J].计算机应用,2004,4(3):88-89 [9] 李杰,徐勇,王云峰.面向个性化推荐的强关联规则挖掘[J].系统工程理论与实践,2009,9(8):133-151 [10] Liu Y Z,Jiang Y C,Liu Y C.CSMC:A combination strategy formulti-class classification based on multiple association rules[J].Knowledge-Based Systems,2008,21(8):786-793 [11] Pang J F,Liang J Y.Evaluation of the results of multi-attribute group decision-making with linguistic information[J].Omega,2012,0(3):294-301 [12] Jiang Y C,Shang J,Liu Y Z.Maximizing customer satisfaction through an online recommendation system:A novel associative classification model[J].Decision Support System,2010,8(3):470-479 [13] Cao F Y,Liang J Y,Li D Y,et al.A dissimilarity measure for the k-Modes clustering algorithm[J].Knowledge-Based Systems,2012,26(1):120-127 [14] 余力,刘鲁.电子商务个性化推荐研究[J].计算机集成制造系统,2004,0(10):1306-1313 [15] Su J H,Wang B W,Hsiao C Y,et al.Personalized rough-set-based recommendation by integrating multiple contents and collaborative information[J].Information Sciences,2010,180(1):113-131 |
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