USER: User-sensitive expert recommendations for knowledge-dense environments

Colin DeLong, Prasanna Desikan, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Traditional recommender systems tend to focus on e-commerce applications, recommending products to users from a large catalog of available items. The goal has been to increase sales by tapping into the user's interests by utilizing information from various data sources to make relevant recommendations. Education, government, and policy websites face parallel challenges, except the product is information and their users may not be aware of what is relevant and what isn't. Given a large, knowledge-dense website and a non-expert user searching for information, making relevant recommendations becomes a significant challenge. This paper addresses the problem of providing recommendations to non-experts, helping them understand what they need to know, as opposed to what is popular among other users. The approach is user-sensitive in that it adopts a 'model of learning' whereby the user's context is dynamically interpreted as they browse and then leveraging that information to improve our recommendations.

Original languageEnglish (US)
Title of host publicationAdvances in Web Mining and Web Usage Analysis - 7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005, Revised Papers
Pages77-95
Number of pages19
StatePublished - Dec 1 2006
Event7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005 - Chicago, IL, United States
Duration: Aug 21 2005Aug 21 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4198 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005
CountryUnited States
CityChicago, IL
Period8/21/058/21/05

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