[1] Yu Fang, Chen Dongling, Wang Daling, et al. User-oriented web search based on PLSA [J]. Journal of Southeast University (English Edition), 2007, 23 (3): 347-351. [doi:10.3969/j.issn.1003-7985.2007.03.007]

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User-oriented web search based on PLSA()

基于PLSA的面向用户的网络搜索

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

23

Issue:

2007 3

Page:

347-351

Research Field:

Computer Science and Engineering

Publishing date:

2007-09-30

Info

Title:

User-oriented web search based on PLSA

基于PLSA的面向用户的网络搜索

Author(s):

Yu Fang¹;  Chen Dongling¹;  2;  Wang Daling¹;  Yu Ge¹;  Bao Yubin¹

¹College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
²School of Information, Shenyang University, Shenyang 110044, China

于芳¹;  陈冬玲¹;  2;  王大玲¹;  于戈¹;  鲍玉斌¹

¹东北大学信息科学与工程学院, 沈阳 110004; ²沈阳大学信息学院, 沈阳 110044

Keywords:

user-oriented search;  underlying search intention;  probabilistic latent semantic analysis(PLSA);  user profile;  topics of interest

面向用户的搜索;  潜在搜索意图;  概率潜在语义分析(PLSA);  用户模型;  兴趣主题

PACS:

TP311

DOI:

10.3969/j.issn.1003-7985.2007.03.007

Abstract:

In order to solve the problem that current search engines provide query-oriented searches rather than user-oriented ones, and that this improper orientation leads to the search engines’ inability to meet the personalized requirements of users, a novel method based on probabilistic latent semantic analysis(PLSA)is proposed to convert query-oriented web search to user-oriented web search.First, a user profile represented as a user’s topics of interest vector is created by analyzing the user’s click through data based on PLSA, then the user’s queries are mapped into categories based on the user’s preferences, and finally the result list is re-ranked according to the user’s interests based on the new proposed method named user-oriented PageRank(UOPR).Experiments on real life datasets show that the user-oriented search system that adopts PLSA takes considerable consideration of user preferences and better satisfies a user’s personalized information needs.

针对当前的搜索引擎提供面向查询、而非面向用户的服务, 从而导致搜索引擎无法满足用户个性化的需求这一问题, 提出了一种基于PLSA的新方法, 将面向查询词的搜索转变成面向用户的搜索.首先, 通过分析用户查询历史和浏览记录建立代表用户模型的用户兴趣向量, 在用户发出查询时用户的查询词根据用户兴趣向量被映射到兴趣分类上, 最终根据面向用户排序算法将返回结果列表重新排序.实验表明该面向用户搜索系统能够充分考虑用户的偏好, 从而更好地满足不同用户的信息需求.

References:

[1] Pretschner Alexander, Gauch Susan.Ontology based personalized search[C]//Proc of the 11th IEEE International Conference on Tools with Artificial Intelligence.Chicago, 1999:391-398.
[2] Liu Fang, Yu Clement, Meng Weiyi.Personalized web search for improving retrieval effectiveness[J].IEEE Trans on Knowledge and Data Engineering, 2004, 16(1):28-40.
[3] Sugiyama K, Hatano K, Yoshikawa M.Adaptive web search based on user profile constructed without any effort from users[C]//Proc of the 13th International World Wide Web Conference.New York:ACM Press, 2004:675-684.
[4] Hofmann T.Unsupervised learning by probabilistic latent semantic analysis[J].Machine Learning, 2001, 42(1/2):177-196.
[5] Hofmann T.Probabilistic latent semantic indexing [C]//Proc of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM Press, 1999:50-57.
[6] Hofmann T.Latent semantic models for collaborative filtering [J].ACM Trans on Information Systems, 2004, 22(1):89-115.
[7] Cohn D, Chang H.Learning to probabilistically identifying authoritative documents[C]//Proc of the 17th International Conference on Machine Learning. San Francisco:Morgan Kaufmann, 2000:167-174.
[8] Jin X, Zhou Y, Mobasher B.Web usage mining based on probabilistic latent semantic analysis[C]//Proc of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press, 2004:197-205.
[9] Lin Chenxi, Xue Guirong, Zeng Huajun, et al.Using probabilistic latent semantic analysis for personalized web search[C]//Proc of the Seventh Asia-Pacific Web Conference (APWeb2005).Shanghai, 2005, 3399:707-717.
[10] Dempster A, Laird N, Rubin D.Maximum likelihood from incomplete data via the EM algorithm[J].Journal of Royal Statistical Society B, 1977, 39(1):1-38.
[11] Haveliwala T H.Topic-sensitive pagerank[C]//Proc of the 11th International World Wide Web Conference.New York:ACM Press, 2002:517-526.
[12] Page Larry, Brin Sergey, Motwani R, et al.The PageRank citation ranking:bringing order to the web[R].California:Stanford Digital Library Technologies Project, 1998.
[13] Baeza-Yates R, Ribeiro-Neto B.Modern information retrieval[M].Beijing:China Machine Press, 2004:82-84.

Memo

Memo:

Biographies: Yu Fang(1981—), female, graduate;Wang Daling(corresponding author), female, doctor, professor, dlwang@mail.neu.edu.cn.

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