[1]宋毅,徐志明.个性化搜索用户兴趣更新学习及评价研究[J].计算机技术与发展,2018,28(06):64-66.[doi:10.3969/ j. issn.1673-629X.2018.06.014]
　SONG Yi,XU Zhi-ming.Research on Personalized Search User Interest Updating Learning and Evaluation[J].,2018,28(06):64-66.[doi:10.3969/ j. issn.1673-629X.2018.06.014]
点击复制

个性化搜索用户兴趣更新学习及评价研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年06期
页码:
64-66
栏目:
智能、算法、系统工程
出版日期:
2018-06-10

文章信息/Info

Title:
Research on Personalized Search User Interest Updating Learning and Evaluation
文章编号:
1673-629X(2018)06-0064-03
作者:
宋毅1 ; 徐志明2
1. 哈尔滨华德学院 电子与信息工程学院 计算机应用技术系,黑龙江 哈尔滨 150025;
2. 哈尔滨工业大学 计算机学院,黑龙江 哈尔滨 150025
Author(s):
SONG Yi 1 ; XU Zhi-ming 2
1. Department of Computer Application and Technology,School of Electronic Information Engineering,Harbin Huade University,Harbin 150025,China;
2. School of Computer,Harbin Institute of Technology,Harbin 150025,China
关键词:
搜索; 兴趣; 数据挖掘; 学习; 评价
Keywords:
search; interest; data mining; learning; evaluation
分类号:
TP302
DOI:
10.3969/ j. issn.1673-629X.2018.06.014
文献标志码:
A
摘要:
提出了一种自适应的用户兴趣模型更新学习及评价方法。 为了给用户提供更精准的查询结果,将用户兴趣模型加入自适应调整算法后进行验证,研究通过分析用户短期兴趣、长期兴趣规律,成为该系统建立用户的兴趣模型可能。 随着时间等的变化,用户兴趣也会发生相应变化。 通过自适应学习过程,为了更好地识别用户感兴趣的信息,通过研究规律进行总结分析。 对兴趣学习技术进行研究,同时对该算法进行了评价。 主要计算了查准率等参数,为此通过评价得出该用户兴趣挖掘精准率较好,对于现代计算机网络购物,以及网络应用过程挖掘用户行为和兴趣提供了良好的方案,也为个性化推荐应用提供了帮助。
Abstract:
An adaptive updating learning and evaluating method for user interest model is proposed. In order to provide users with more accurate search results,the user interest model is verified after adding adaptive adjustment algorithm. Through the analysis of user shortterm interest and long-term interest in law,it becomes interested in model of users of the system. With the change of time,the user’s interests will change accordingly. We analyze the algorithm of user’s interest by the adaptive learning process,in which the rules change,
so as to obtain the user’s interest points. We also research on interest learning technology and evaluate it. Main parameters like precision is calculated,and the evaluation shows the user interest mining precision rate is better,providing a well solution for the modern computer network shopping and network application and process of mining user behavior and interest,with aid to personalized recommendation application.

相似文献/References:

[1]张春生 庄丽艳.基于兴趣的Web挖掘中用户身份的识别新方法[J].计算机技术与发展,2009,(05):62.
　ZHANG Chun-sheng,ZHUANG Li-yan.New Method on Identification of User in Web Mining Based on Interesting[J].,2009,(06):62.
[2]张创业 莫愿斌 何登旭.二群协同的人工鱼群优化算法[J].计算机技术与发展,2009,(11):38.
　ZHANG Chuang-ye,MO Yuan-bin,HE Deng-xu.Two Swarms Cooperative Artificial Fish- Swarm Optimization Algorithm[J].,2009,(06):38.
[3]张培阳 糜正琨 徐名海 黄光平.融合地址簿系统的反向搜索实现技术[J].计算机技术与发展,2010,(07):93.
　ZHANG Pei-yang,MI Zheng-kun,XU Ming-hai,et al.Implementation Technology of Reverse Search for Converged Address Book[J].,2010,(06):93.
[4]孙晓莉 禹继国.基于特殊数列的懒洪泛P2P搜索技术[J].计算机技术与发展,2010,(07):117.
　SUN Xiao-li,YU Ji-guo.New Lazy Flooding Searching Techniques Based on Special Progressions in Unstructured P2P Networks[J].,2010,(06):117.
[5]郭玉龙 吴晓军.蚁群算法在P2P搜索中的应用研究[J].计算机技术与发展,2011,(10):55.
　GUO Yu-long,WU Xiao-jun.Research on Search Based on Ant Colony Algorithm in P2P Networks[J].,2011,(06):55.
[6]张俊,李鲁群,周熔.基于Lucene的搜索引擎的研究与应用[J].计算机技术与发展,2013,(06):230.
　ZHANG Jun,LI Lu-qun,ZHOU Rong.Research and Application of Search Engine Based on Lucene[J].,2013,(06):230.
[7]何绯娟,缪相林,许大炜,等. 基于“读者-图书”二部图的个性化图书推荐方法[J].计算机技术与发展,2015,25(05):25.
　HE Fei-juan,MIAO Xiang-lin,XU Da-wei,et al. Personalized Recommendation of Books Based on “Reader-Book”Bipartite Graph[J].,2015,25(06):25.
[8]高倩[],何聚厚[]. 改进的面向数据稀疏的协同过滤推荐算法[J].计算机技术与发展,2016,26(03):63.
　GAO Qian[],HE Ju-hou[]. An Improved Collaborative Filtering Recommendation Algorithm for Data Sparsity[J].,2016,26(06):63.

更新日期/Last Update: 2018-08-16