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» Supporting User Query Relaxation in a Recommender System
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133
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CSCW
2006
ACM
15 years 10 months ago
Don't look stupid: avoiding pitfalls when recommending research papers
If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research paper...
Sean M. McNee, Nishikant Kapoor, Joseph A. Konstan
113
Voted
ENTCS
2006
123views more  ENTCS 2006»
15 years 4 months ago
Wrapping of Web Sources with restricted Query Interfaces by Query Tunneling
Information sources in the World Wide Web usually offer two different schemes to their users, an Interface Schema which the user can query and a Result Schema which the user can b...
Thomas Kabisch, Mattis Neiling
164
Voted
ACMICEC
2008
ACM
272views ECommerce» more  ACMICEC 2008»
15 years 6 months ago
Adapting the interaction state model in conversational recommender systems
Conventional conversational recommender systems support interaction strategies that are hard-coded into the system in advance. In this context, Reinforcement Learning techniques h...
Tariq Mahmood, Francesco Ricci
SIGIR
2011
ACM
14 years 7 months ago
Functional matrix factorizations for cold-start recommendation
A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressivel...
Ke Zhou, Shuang-Hong Yang, Hongyuan Zha
ADAPTIVE
2007
Springer
15 years 10 months ago
Collaborative Filtering Recommender Systems
One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items th...
J. Ben Schafer, Dan Frankowski, Jonathan L. Herloc...