CiteULike: Group: Adaptive-Web - with tag implicit-feedback

Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search

Thorsten Joachims — 2008-01-04T21:39:32-00:00

ACM Trans. Inf. Syst., Vol. 25, No. 2. (April 2007)

Relevance feedback from eye movements for proactive information retrieval

S Arvi — 2008-02-06T18:51:30-00:00

(2004)

Introduction. This on-going work is part of a larger project that aims at complementing or even replacing the laborious explicit relevance feedback in information retrieval by implicit feedback. Technology for measuring eye movements begins to be mature enough, and the movements contain rich information about the attention and interest patterns of the user. The problem is that the signal is very noisy and the correspondence of the eye xation patterns to user's attention is not always...

Display time as implicit feedback: understanding task effects

Diane Kelly — 2007-10-26T20:03:08-00:00

(2004), pp. 377-384.

Implicit user profiling for on demand relevance feedback

Yoshinori Hijikata — 2007-03-28T04:24:45-00:00

(2004), pp. 198-205.

Click data as implicit relevance feedback in web search

Seikyung Jung — 2007-09-13T14:54:13-00:00

Inf. Process. Manage., Vol. 43, No. 3. (May 2007), pp. 791-807.

Using annotations in enterprise search

Pavel Dmitriev — 2007-02-04T19:28:07-00:00

(2006), pp. 811-817.

A major difference between corporate intranets and the Internet is that in intranets the barrier for users to create web pages is much higher. This limits the amount and quality of anchor text, one of the major factors used by Internet search engines, making intranet search more difficult. The social phenomenon at play also means that spam is relatively rare. Both on the Internet and in intranets, users are often willing to cooperate with the search engine in improving the search experience. These characteristics naturally lead to considering using user feedback to improve search quality in intranets. In this paper we show how a particular form of feedback, namely user annotations, can be used to improve the quality of intranet search. An annotation is a short description of the contents of a web page, which can be considered a substitute for anchor text. We propose two ways to obtain user annotations, using explicit and implicit feedback, and show how they can be integrated into a search engine. Preliminary experiments on the IBM intranet demonstrate that using annotations improves the search quality.

Evaluating implicit feedback models using searcher simulations

Ryen White — 2006-11-30T22:10:07-00:00

ACM Trans. Inf. Syst., Vol. 23, No. 3. (July 2005), pp. 325-361.

A case for interaction: a study of interactive information retrieval behavior and effectiveness

J&\#252;rgen Koenemann — 2005-10-14T16:47:25-00:00

(1996), pp. 205-212.

SIGIR 2003 workshop report: implicit measures of user interests and preferences

Susan Dumais — 2006-08-07T20:19:33-00:00

SIGIR Forum, Vol. 37, No. 2. (2003), pp. 50-54.

Accurately interpreting clickthrough data as implicit feedback

Thorsten Joachims — 2005-11-03T14:08:57-00:00

(2005), pp. 154-161.

This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.

Unobtrusive Data Collection for Web-Based Social Navigation

Katja Hofmann — 2006-06-18T11:24:16-00:00

(2006)

In initial laboratory studies, subsymbolic user behavior has shown promise as a source of information for social navigation. Scalable, unobtrusive methods are needed for acquiring data on subsymbolic user behavior in field studies or live systems. Current methods are not suitable for use outside the laboratory because they interfere with normal user behavior and environment. We present a method for unobtrusively collecting subsymbolic user behavior in web-based systems, and report results from a field study. Our method is unobtrusive in that it uses current web technologies, works on the vast majority of current browsers, requires minimal instrumentation of existing web-based systems, and requires no additional user effort. This unobtrusive data collection method paves the way for future research on using subsymbolic user behavior to improve social navigation.

Improving the Quality of the Personalized Electronic Program Guide

Derry Oâsullivan — 2006-11-07T16:20:21-00:00

User Modeling and User-Adapted Interaction, Vol. V14, No. 1. (1 February 2004), pp. 5-36.

Evaluating implicit measures to improve web search

Steve Fox — 2006-05-16T20:17:32-00:00

ACM Trans. Inf. Syst., Vol. 23, No. 2. (April 2005), pp. 147-168.

A Two-Level Adaptive Visualization for Information Access to Open-Corpus Educational Resource

Jae-Wook Ahn — 2006-06-18T10:27:13-00:00

(2006)

The labyrinthine abundance of educational resources on the Web has greatly expanded the challenge of helping students find, organize, and use resources that will best match their individual goals, interests, and current knowledge. Map-based navigation, using technologies such as the Self-Organizing Maps (SOM), is one solution to this growing challenge. However, as the number of documents organized by SOM increases, the number of documents within each cell becomes too large for the user to make meaningful choices, overwhelming his ability to make accurate decisions. Combining interactivity with the ability to organize a large number of documents, we have developed two-level heterogeneous maps that are augmented with social navigation support. We implemented our idea within the Knowledge Sea II system and ran a pilot study using this system in an Information Retrieval course.

Implicit feedback for inferring user preference: a bibliography

Diane Kelly — 2005-08-10T19:20:47-00:00

SIGIR Forum, Vol. 37, No. 2. (2003), pp. 18-28.