Log Workshop Presentation 2009a

Moving from Description to Prediction for Information Searching Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address] Information searching: actions (behavioral, affective, and cognitive) employed by people when interacting with an information system Information Searching

Who is Jim Jansen? ,[object Object],[object Object],[object Object],[object Object]

What is the information context that we are facing?

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Explosion of Information - the Zettabytes are coming

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data  Information  Knowledge What about wisdom? Is Twitter making us smarter? (http://tinyurl.com/clya82)

Jim Jansen’s Research ,[object Object],[object Object],[object Object],Will primarily address the algorithmic work, but end with a summary slide of affective, cognitive, and business research projects. Twitter search logs

The Power of Search and the Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sources: comScore, U.S., Feb. ’06, Stanford Institute for the Quantitative Study of Society, Nov. ‘05

Analysis of Search Marketplace Holding fairly stable over the last year or so

Analysis of Online Traffic Long tail for online traffic (i.e., a few sites with a lot of traffic and a whole bunch will little traffic)

Analysis of Keyword Advertising ,[object Object],[object Object],[object Object],Some of the most detailed user behavioral research current going on – almost all outside of academic and research firms!

State of Information Searching Research ,[object Object],[object Object],[object Object],Important for a lot of reasons – from technology development, system resource allocation, trends, extreme events, financial, and understanding users

Information Searching ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Note: not always ‘informational’ anymore. Many time people are searching for ‘ other things ’. Jansen, Booth, Spink (2008).

Illustration of Probabilistic User Modeling Using n-grams Given these states … … how accurately can we predict these? AC 5 A 4 ABCDE 3 ABCDE 2 ABCF 1 Search State Transitions User 40% D C 60% B A 100% E CD 66% D BC 1OO% C AB Accuracy Next State? Predictive Pattern

Example Using Search Log ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Jansen, B. J., Booth, D. L., & Spink, A. (Forthcoming). Patterns of query modification during Web searching . Journal of the American Society for Information Science and Technology . Not much better than just guessing! 0 1 st 2 nd 3 rd 4 th Order of the Model Accuracy of Prediction 0.1 0.2 0.3 0.4 0.5 0.6 0.28 0.40 0.47 0.44 0.44 0.60 Drop out rate (folks who don’t submit a query ~40%)

Search engine logs as an information stream (voluminous, temporal, and multi-dimensional) Server Server Server Search Engines Servers Search Attributes Time 0 th period 1 st period … period n th period General Idea Information searching is a temporal stream (i.e., stateless)

Search Engine Logs – viewed as a temporal stream (i.e., stateless, with volume, mass, momentum, and acceleration) Search Engines Servers Search Attributes Time 0 th period 1 st period … period n th period General Idea What if, based on what has happened in the past in the temporal stream, … we could predict what is going to happen in the future?

Ongoing Research and Challenges Gopalakrish & Jansen (Under Review) ,[object Object],[object Object],Tensor Analysis Zhang, Y. & Jansen (2009) - session duration, query length, query reformulation correlate positively with future clickthrough Neural Networks Zhang, Y. & Jansen (2009) - inference between query length and ranked of clicked result Time Series Analysis Kathuria & Jansen (Working) ,[object Object],K-means Clustering Jansen, Booth, & Spink (2008) ,[object Object],[object Object],Decision Tree Jansen & Zhang, M. (2008) - 1 st or 2 nd order models work best N-grams Publication Implications Method These methods are valuable in some situations. However, none of these methods are really effective for analyzing temporal, voluminous, and multi-dimensional logs  need more robust method of analysis. This aim is the focus of much of my algorithmic research.

Lets take a look at some other research work ,[object Object],[object Object],[object Object]

Lets take a look at some other work ,[object Object],[object Object],[object Object]

Research and Online Presence ,[object Object],[object Object],[object Object],[object Object]

Thank you! (open for questions and further discussion) Jim Jansen College of Information Sciences and Technology The Pennsylvania State University [email_address]

Search Logs has some common fields, such as time, queries, results, etc. We can enrich the log with additional fields. Back

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Log Workshop Presentation 2009a

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Log Workshop Presentation 2009a