Studying Ransomware Attacks Using Web Search Logs

We investigate the automatic generation of topic pages as an alternative to the current Web search paradigm. Topic pages explicitly aggregate information across documents, filter redundancy, and promote diversity of topical aspects. We propose a novel framework for building rich topical aspect models and selecting diverse information from the Web. In particular, we use Web search logs to build aspect models with various degrees of specificity, and then employ these aspect models as input to a sentence selection method that identifies relevant and non-redundant sentences from the Web. Automatic and manual evaluations on biographical topics show that topic pages built by our system compare favorably to regular Web search results and to MDS-style summaries of the Web results on all metrics employed.

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Using web search logs to identify query classification terms

International Journal of Web Information Systems ◽

10.1108/17440080710848107 ◽

2007 ◽

Vol 3 (4) ◽

pp. 315-327 ◽

Cited By ~ 1

Author(s):

Isak Taksa ◽

Sarah Zelikovitz ◽

Amanda Spink

Keyword(s):

Web Search ◽

Query Classification ◽

Search Logs

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Processing and Analysis of Search Query Logs in Chinese

Handbook of Research on Web Log Analysis ◽

10.4018/978-1-59904-974-8.ch019 ◽

2011 ◽

pp. 378-388 ◽

Cited By ~ 1

Author(s):

Michael Chau ◽

Yan Lu ◽

Xiao Fang ◽

Christopher C. Yang

Keyword(s):

World Wide ◽

Web Search ◽

Searching Behavior ◽

Web Searching ◽

Search Queries ◽

Web Search Engine ◽

The World ◽

Query Logs ◽

Search Logs ◽

The Web

More non-English contents are now available on the World Wide Web and the number of non-English users on the Web is increasing. While it is important to understand the Web searching behavior of these non-English users, many previous studies on Web query logs have focused on analyzing English search logs and their results may not be directly applied to other languages. In this Chapter we discuss some methods and techniques that can be used to analyze search queries in Chinese. We also show an example of applying our methods on a Chinese Web search engine. Some interesting findings are reported.

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Detecting Hot Events from Web Search Logs

Web-Age Information Management - Lecture Notes in Computer Science ◽

10.1007/978-3-642-14246-8_41 ◽

2010 ◽

pp. 417-428 ◽

Cited By ~ 1

Author(s):

Yingqin Gu ◽

Jianwei Cui ◽

Hongyan Liu ◽

Xuan Jiang ◽

Jun He ◽

...

Keyword(s):

Web Search ◽

Search Logs

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Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization

ACM Transactions on Information Systems ◽

10.1145/3476106 ◽

2022 ◽

Vol 40 (3) ◽

pp. 1-24

Author(s):

Jiashu Zhao ◽

Jimmy Xiangji Huang ◽

Hongbo Deng ◽

Yi Chang ◽

Long Xia

Keyword(s):

Probabilistic Model ◽

Large Scale ◽

Web Search ◽

Latent Dirichlet Allocation ◽

State Of The Art ◽

User Profile ◽

New Approach ◽

Latent Topic ◽

Search History ◽

Search Logs

In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user’s search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user’s search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user’s search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models.

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The Methodology of Search Log Analysis

Handbook of Research on Web Log Analysis ◽

10.4018/978-1-59904-974-8.ch006 ◽

2011 ◽

pp. 100-123 ◽

Cited By ~ 2

Author(s):

Bernard J. Jansen

Keyword(s):

Information System ◽

System Design ◽

Web Search ◽

Log Analysis ◽

Web Searching ◽

Information Searching ◽

Information System Design ◽

Analysis Methodology ◽

Three Stages ◽

Search Logs

Exploiting the data stored in search logs of Web search engines, Intranets, and Websites can provide important insights into understanding the information searching tactics of online searchers. This understanding can inform information system design, interface development, and information architecture construction for content collections. This chapter presents a review of and foundation for conducting Web search transaction log analysis. A search log analysis methodology is outlined consisting of three stages (i.e., collection, preparation, and analysis). The three stages of the methodology are presented in detail with discussions of the goals, metrics, and processes at each stage. The critical terms in transaction log analysis for Web searching are defined. Suggestions are provided on ways to leverage the strengths and addressing the limitations of transaction log analysis for Web searching research.

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