Orienteering in an information landscape: how information seekers get from here to there
INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
Communications of the ACM
Information Retrieval: Algorithms and Heuristics (The Kluwer International Series on Information Retrieval)
Information search and re-access strategies of experienced web users
WWW '05 Proceedings of the 14th international conference on World Wide Web
Why we search: visualizing and predicting user behavior
Proceedings of the 16th international conference on World Wide Web
On the value of temporal information in information retrieval
ACM SIGIR Forum
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Predicting query performance using query, result, and user interaction features
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Proceedings of the 20th ACM international conference on Information and knowledge management
Evaluating the effectiveness of search task trails
Proceedings of the 21st international conference on World Wide Web
Modeling and predicting behavioral dynamics on the web
Proceedings of the 21st international conference on World Wide Web
Search, interrupted: understanding and predicting search task continuation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Leaving so soon?: understanding and predicting web search abandonment rationales
Proceedings of the 21st ACM international conference on Information and knowledge management
Expediting search trend detection via prediction of query counts
Proceedings of the sixth ACM international conference on Web search and data mining
Clustering Time Series Using Unsupervised-Shapelets
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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Analyzing people's Web search behavior has been a significant topic of interest in the Information Retrieval domain and search engine industry over the past decade. Research in this area has focused on improving search and retrieval capabilities leading to high demands and expectations of Web search users. Understanding and analyzing the Web search process when users are performing Web search tasks is a challenging problem due to many reasons such as subjectivity, dynamic nature, difficulty in measurement of success and difficulty in evaluation. I propose to analyze the users' Web search behavior in order to identify the strategies and tactics they use in fulfilling their task. In order to achieve this, I intend to use data mining and machine learning methods with an emphasis on time series analysis given that the user search process can be considered as a sequence of time related events.