Mining clickthrough data for collaborative web search

  title={Mining clickthrough data for collaborative web search},
  author={Jian-Tao Sun and Xuanhui Wang and Dou Shen and Hua-Jun Zeng and Zheng Chen},
  booktitle={WWW '06},
This paper is to investigate the group behavior patterns of search activities based on Web search history data, i.e., clickthrough data, to boost search performance. We propose a Collaborative Web Search (CWS) framework based on the probabilistic modeling of the co-occurrence relationship among the heterogeneous web objects: users, queries, and Web pages. The CWS framework consists of two steps: (1) a cube-clustering approach is put forward to estimate the semantic cluster structures of the Web… Expand
Learning-based web query understanding
This thesis focuses on personal name detection in Web queries and proposes three solutions for different scenarios in query topic classification, which reflect the generalization/specification relationship among Web queries. Expand
Collaborative Web Search Based on User Interest Similarity
Empirical evaluation showed that the interaction among personal agents increases the performance of the overall recommender system, demonstrating the potential of the approach to reduce the burden of finding information on the Web. Expand
Improving Cloaking Detection using Search Query Popularity and Monetizability
It is shown that the degree of cloaking among search results depends on query properties such as popularity and monetizability, and a new measure for detecting cloaked URLs is presented that uses a normalized term frequency ratio between multiple downloaded copies of Web pages. Expand
Personalization of tagging systems
This paper introduces a framework for the personalization of social media systems and proposes a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to theindividual user preferences. Expand
Group Crumb: Sharing Web Navigation by Visualizing Group Traces on the Web
Results show that making group navigation traces available on Web pages to group members increases their Web information foraging performance, promotes group collaboration, and enhances their Web browsing user experience as well. Expand
The Popularity of Articles in PubMed
The PubMed search engine displays query results in reverse chronological order, which is appropriate for users interested in the latest publications. The purpose of this paper is to use machineExpand
Enhancing group awareness on the web: Prototype and experiments of sharing web page visitation information among teammates
This paper proposes a novel approach to sharing web page visitation information among teammates, named Shared Browsing History, and describes two user studies that show that the approach was effective in enhancing participants' group awareness and improved group collaborative efficiency in programming and software development tasks. Expand
Adversarial information retrieval on the web (AIRWeb 2006)
The attraction of hundreds of millions of web searches per day provides significant incentive for many content providers to do whatever is necessary to rank highly in search engine results, whileExpand
Adversarial Information Retrieval on the Web (AIRWeb 2007)
The ubiquitous use of search engines to discover and access Web content shows clearly the success of information retrieval algorithms. However, unlike controlled collections, the vast majority of WebExpand


A Live-User Evaluation of Collaborative Web Search
The results of a large-scale evaluation of collaborative Web search, in a corporate Web search scenario, shows that significant benefits are available to its users. Expand
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains. Expand
Information-theoretic co-clustering
This work presents an innovative co-clustering algorithm that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages and demonstrates that the algorithm works well in practice, especially in the presence of sparsity and high-dimensionality. Expand