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» Improving Case-Based Recommendations Using Implicit Feedback
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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
11 years 7 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
CCIA
2005
Springer
13 years 10 months ago
Acquiring Unobtrusive Relevance Feedback through Eye-Tracking in Ambient Recommender Systems
Acquiring relevant information to keep user’s preferences up-to-date is crucial in recommender systems in order to close the cycle of recommendations. Ambient Intelligence is a s...
Gustavo González, Beatriz López, Cec...
UM
2009
Springer
13 years 11 months ago
How Users Perceive and Appraise Personalized Recommendations
Abstract. Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone...
Nicolas Jones, Pearl Pu, Li Chen
ICWE
2005
Springer
13 years 10 months ago
Automatic Optimization of Web Recommendations Using Feedback and Ontology Graphs
Abstract. Web recommendation systems have become a popular means to improve the usability of web sites. This paper describes the architecture of a rulebased recommendation system a...
Nick Golovin, Erhard Rahm
ECIR
2008
Springer
13 years 6 months ago
Use of Implicit Graph for Recommending Relevant Videos: A Simulated Evaluation
In this paper, we propose a model for exploiting community based usage information for video retrieval. Implicit usage information from a pool of past users could be a valuable sou...
David Vallet, Frank Hopfgartner, Joemon M. Jose