| 基于云模型熟悉相似度的协同过滤推荐算法 |
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| 引用本文： | 汪军,朱建军,覃朗.基于云模型熟悉相似度的协同过滤推荐算法[J].计算机工程与科学,2017,39(11):2102-2108. |
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| 作者姓名： | 汪军 朱建军 覃朗 |
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| 作者单位： | ;1.南京航空航天大学经济与管理学院 |
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| 基金项目： | 国家自然科学基金（71171112，71401064,71502073）；教育部人文社科基金(14YJC630120)； 南京航空航天大学研究生开放基金(kfjj20150903) |
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| 摘 要： | 研究了一种新的协同过滤推荐方法。针对推荐算法中相似度存在的不足,提出了兼顾"形状-距离"的云模型综合相似度测算方法;考虑用户之间的兴趣匹配,提出了云模型熟悉相似度的概念;提出了基于云模型熟悉相似度的邻居用户选择方法,进而产生推荐。实验结果表明,本方法提高了推荐准确度。
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| 关 键 词： | 推荐算法 协同过滤 云模型相似度 熟悉度 |
| 收稿时间： | 2016-01-07 |
| 修稿时间： | 2017-11-25 |
A collaborative filtering recommendation algorithm
based on familiarity similarity of cloud model |
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| WANG Jun,ZHU Jian-jun,QIN Lang.A collaborative filtering recommendation algorithm
based on familiarity similarity of cloud model[J].Computer Engineering & Science,2017,39(11):2102-2108. |
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| Authors: | WANG Jun ZHU Jian-jun QIN Lang |
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| Affiliation: | （School of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China） |
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| Abstract: | In order to overcome the shortcomings of similarity measurement in recommendation algorithms, we propose a new collaborative filtering recommendation algorithm for calculating the integrated similarity of cloud model based on shape similarity and distance similarity. Taking account of the interest of users, we introduce the concept of familiarity similarity of cloud model, based on which the neighbors are identified, thus obtaining the recommendation results. Experimental results show that the proposed method can improve recommendation accuracy . |
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| Keywords: | recommendation algorithm collaborative filtering cloud model similarity familiarity degree |
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