R
Robin Burke
Researcher at University of Colorado Boulder
Publications - 229
Citations - 16489
Robin Burke is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 53, co-authored 216 publications receiving 14276 citations. Previous affiliations of Robin Burke include California State University & California State University, Fullerton.
Papers
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Journal ArticleDOI
Hybrid Recommender Systems: Survey and Experiments
TL;DR: This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
Book ChapterDOI
Hybrid web recommender systems
TL;DR: This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies and finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.
Knowledge-based recommender systems
TL;DR: Recommendations made by recommender systems can help users navigate through large information spaces of product descriptions, news articles or other items, and are an increasingly important tool in the on-line information and e-commerce burgeon.
Proceedings ArticleDOI
Personalized recommendation in social tagging systems using hierarchical clustering
TL;DR: This work presents a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters and presents extensive experimental results on two real world dataset, suggesting that guysonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation.
Journal ArticleDOI
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Robin Burke,Kristian J. Hammond,Vladimir Kulyukin,Steven L. Lytinen,Noriko Tomuro,S. Schoenberg +5 more
TL;DR: This technical report describes FAQ Finder, a natural language question answering system that uses files of frequently asked questions as its knowledge base, and describes the design and the current implementation of the system and its support components.