从偏好数据库中挖掘Ceteris Paribus偏好

doi:10.11772/j.issn.1001-9081.2016.08.2092

计算机应用 ›› 2016, Vol. 36 ›› Issue (8): 2092-2098.DOI: 10.11772/j.issn.1001-9081.2016.08.2092

• 第六届中国数据挖掘会议(CCDM 2016) • 上一篇下一篇

从偏好数据库中挖掘Ceteris Paribus偏好

辛冠琳, 刘惊雷

烟台大学计算机与控制工程学院, 山东烟台 264005

收稿日期:2016-03-01 修回日期:2016-05-05 出版日期:2016-08-10 发布日期:2016-08-10
通讯作者: 刘惊雷
作者简介:辛冠琳(1991-),女,山东泰安人,硕士研究生,主要研究方向:CP-nets图模型的推理与学习;刘惊雷(1970-),男,山西临猗人,副教授,硕士,CCF会员,主要研究方向:人工智能、理论计算机科学。
基金资助:
国家自然科学基金资助项目（61572419,61403328,61403329）；山东省自然科学基金资助项目（ZR2013FM011,2015GSF115009,ZR2014FQ016,ZR2014FQ026）。

Mining Ceteris Paribus preference from preference database

XIN Guanlin, LIU Jinglei

School of Computer and Control Engineering, Yantai University, Yantai Shandong 264005, China

Received:2016-03-01 Revised:2016-05-05 Online:2016-08-10 Published:2016-08-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61572419, 61403328, 61403329), the Natural Science Foundation of Shandong Province (ZR2013FM011, 2015GSF115009, ZR2014FQ016, ZR2014FQ026)

摘要/Abstract

摘要： 针对传统的推荐系统需要用户给出明确的偏好矩阵（U-I矩阵），进而使用自动化技术来获取用户偏好的问题，提出了一种从偏好数据库中挖掘出Agent的偏好信息的方法。从知识发现的角度，通过Ceteris Paribus规则（CP规则），提出了k阶偏好挖掘算法（kPreM）。在算法中，利用k阶CP规则对偏好数据库中的信息进行剪枝处理，减少了数据库扫描次数，从而提高了偏好信息的挖掘效率。随后以一种通用的图模型——条件偏好网（CP-nets）为工具，揭示了用户的偏好可近似表达为CP-nets的定性条件偏好网。实验结果表明，用户的偏好都是带有条件的偏好。另外，通过挖掘得出的CP-nets偏好模型，为设计个性化的推荐系统提供了理论基础。

关键词: 自动化技术, 偏好数据库, 知识发现, CP规则, 定性条件偏好网

Abstract: Focusing on the issue that the traditional recommendation system requires users to give a clear preference matrix (U-I matrix) and then uses automation technology to capture the user preferences, a method for mining preference information of Agent from preference database was introduced. From the perspective of knowledge discovery, a k order preference mining algorithm named kPreM was proposed according to the Ceteris Paribus rules (CP rules). The k order CP rules were used to prune the information in the preference database, which decreased the database scanning times and increased the efficiency of mining preference. Then a general graph model named CP-nets (Conditional Preference networks) was used as a tool to reveal that the user preferences can be approximated by the corresponding CP-nets. The theoretical analysis and simulation results show that the user preferences are conditional preferences. In addition, the xcavation of CP-nets preference model provides a theoretical basis for designing personalized recommendation system.

Key words: automation technology, preference database, knowledge discovery, Ceteris Paribus rule (CP rule), qualitative conditional preference network

中图分类号:

TP181

辛冠琳, 刘惊雷. 从偏好数据库中挖掘Ceteris Paribus偏好[J]. 计算机应用, 2016, 36(8): 2092-2098.

XIN Guanlin, LIU Jinglei. Mining Ceteris Paribus preference from preference database[J]. Journal of Computer Applications, 2016, 36(8): 2092-2098.

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从偏好数据库中挖掘Ceteris Paribus偏好

Mining Ceteris Paribus preference from preference database

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