Archive for UCAIR

PIM Workshop of SIGIR 2006

May 21, 2006 at 11:00 am · Filed under SIGIR, UCAIR, personal information management

At SIGIR 2006, there is a two-day workshop about Personal Information Management (PIM). We submit a paper about capturing and exploiting personal search history to improve retrieval accuracy. Here is the abstract of the submission.

Personal search history is an important type of personal information that is critical for learning a user’s interests and information needs and can be exploited to improve the search service for a user. In this paper, we describe our recent work on User-Centered Adaptive Information Retrieval (UCAIR), which aims at capturing personal search history with a client-side search agent and exploiting the history information to help a user optimize search results.

   

We propose a decision theoretic framework and develop techniques for implicit user modeling based on a user’s personal search history.  We propose several context-sensitive retrieval algorithms based on statistical language models to combine the personal search history with the current query for better ranking of documents. Using these techniques, we have developed an intelligent client-side web search agent, i.e., the UCAIR search agent, which can automatically capture a user’s personal search history, store it in XML format on the local disk, and exploit it to provide personalized search.

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Back Button of Web Browser in Personalization

December 11, 2005 at 12:16 pm · Filed under UCAIR, web browser

UCAIR toolbar changes the semantics of Back button of the web browser. Using Internet Explorer with UCAIR toolbar, when the user clicks one result of search result page and then clicks the Back button, the user will see different contents of search result page. This is because the UCAIR personalized search agent updates the user model immediately after the user makes an action (click a result link) and rerank the search results according to the updated user model. So the user will see reranked search result page, which probably is different from the page previously seen by the user. Thus the semantics of back button has changed after the installation of UCAIR toolbar.

During several demos of UCAIR toolbar, many people are interested in the semantics change of the back button. A lady said she would like to see the same stuff as before after clicking the back button. Some people are interested in how to minimize the confusion brought to the user with the semantics change such as where pushed up results should be places if UCAIR toolbar has to change the semantics of the Back button.

I found the breaking of Back button was considered to be one of top web design mistakes by Jakob Nielsen in 1999.  The semantics of Back button is a question for the web design now, especially with many dynamic web design techniques such as Ajax.   What does the user expect when he clicks the Back button? Probably the answer will not be consistent. There is some research works on the Back button of web browser such as Getting Back to Back by Saul Greenberg and Andy Cockburn.

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A New Version of UCAIR Toolbar

September 22, 2005 at 7:21 am · Filed under UCAIR

There is a new version of UCAIR toolbar, which can be downloaded from the UCAIR project website. This version is rewritten by Bin nearly from scratch. We redesigned the software architecture of UCAIR toolbar, which aims to be extensible and robust.

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UCAIR Personalized Search Toolbar

September 9, 2005 at 8:38 am · Filed under UCAIR

In UCAIR project, we develop a UCAIR Personalized Search Toolbar. The software can be downloaded from UCAIR project website. Currently, UCAIR Toolbar is an Internet Explorer plug-in and uses Google search results as basic results.  But it is a matter of engineering to integrate it with other web browsers such as Firefox or use other search engines such as Yahoo search results.

Compared with the personalization at the search engine server side, personalization at the client side as UCAIR toolbar does has the following advantages. 1) Privacy is a much less concern. The user interaction history will be strictly kept at the client side and the search engine can not store the information about what you have viewed. 2) On the client side, there is much richer user information than just keyword query and clickthrough data, which can be used to better infer the user model. For example, the user local files can be indexed to represent the user information interest. 3) The computation and storage cost will be reduced on the search engine side. The disadvantage I can see for the personalization at the client side is that there is no global index for all web pages so that the client side probably can not control the general retrieval function.

comScore has a report about  Search Engine rating in July 2005. Not surprisingly, Google maintains the lead with 36.5% share of search following Yahoo (30.5%) and MSN (15.5%). But for the search submitted from toolbar, Yahoo tops the share. “Yahoo toolbars processed more than 282 million searches during the month, a 74-percent increase over the previous year”.  A more interesting number related with the personalization at the client side is the ratio of searches submitted from toolbar over all searches. “In July, 11 percent of all domestic searches were conducted via toolbars, up from 8 percent in July 2004.”  From this number, we can see that indeed a lot of searches (11%) are submitted from toolbar and if the personalization functionality is added into the toolbar, this percentage number is expected to increase since the user will see more relevant web pages returned to them using the personalized search toolbar to do search [See our CIKM 2005 paper for a user study about personalized web search].

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SIGIR 2005 Papers from UCAIR project

September 2, 2005 at 9:52 pm · Filed under SIGIR, UCAIR

There are two papers from UCAIR project published in SIGIR 2005 . The title of one paper is Active Feedback in Ad-hoc Information Retrieval by Xuehua Shen and ChengXiang Zhai. This work studies how to do document selection for user relevance judgment if the user is willing to judge relevance of some documents, while the traditional relevance feedback is focused on how to do query term expansion and query term reweighting given user judged feedback documents. We propose a preliminary framework and several active feedback methods in this paper; the title of the other paper is Context-Sensitive Information Retrieval Using Implicit Feedback by Xuehua Shen, Bin Tan and ChengXiang Zhai. This work studies how to model user interaction history (implicit feedback) to improve retrieval accuracy. We propose four statistical contextual language models to incorporate context information.

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CIKM 2005 Paper from UCAIR Project

September 1, 2005 at 9:55 pm · Filed under CIKM, UCAIR

A common major limitation of existing retrieval models and systems is that the retrieval decision is, in general, based solely on the query and document collection; information about the actual user and the search context is largely ignored. This limitation makes the retrieval performance of existing IR systems inherently non-optimal, as seen clearly in the following two cases:

  • Different users may use exactly the same query to search for different information, but existing IR systems return the same results for these users. For example, the query “IR applications” on Google returns a mixture of documents about “information retrieval” applications and “infrared” applications, as “IR” can be an acronym for both information retrieval and infrared. Without considering the actual user it is inherently impossible to know which sense “IR” refers to.
  • A user’s information needs may change over time. The same user may sometimes use “java” to mean the Java island and some other times use “java” to mean the programming language. Without recognizing the search context, it would be again inherently impossible to recognize the correct sense.

It is therefore clear that an optimal retrieval system must incorporate both user information and search context into the retrieval decision process.

The UCAIR ( pronounced as “you care”, means User Centered Adaptive Information Retrieval) project seeks to break this limitation of the existing retrieval methods and formally develop a new retrieval paradigm called user-centered adaptive information retrieval (UCAIR), in which user information and search context are both exploited to improve retrieval performance.

Here is a paper which will be published in CIKM 2005 . The paper is Implicit User Modeling for Personalized Search  by Xuehua Shen, Bin Tan, and ChengXiang Zhai. There are two main contributions of this paper. One is to propose a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. The other is to develop and evaluate a client-side personalized search agent UCAIR. 

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