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» Accuracy in Rating and Recommending Item Features
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RECSYS
2009
ACM
14 years 2 months ago
Recommending new movies: even a few ratings are more valuable than metadata
In the Netflix Prize competition many new collaborative filtering (CF) approaches emerged, which are excellent in optimizing the RMSE of the predictions. Matrix factorization (M...
István Pilászy, Domonkos Tikk
RECSYS
2010
ACM
13 years 8 months ago
Recommending based on rating frequencies
Since the development of the comparably simple neighborhood-based methods in the 1990s, a plethora of techniques has been developed to improve various aspects of collaborative fil...
Fatih Gedikli, Dietmar Jannach
IPM
2007
182views more  IPM 2007»
13 years 7 months ago
A probabilistic music recommender considering user opinions and audio features
A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation...
Qing Li, Sung-Hyon Myaeng, Byeong Man Kim
ECWEB
2011
Springer
233views ECommerce» more  ECWEB 2011»
12 years 7 months ago
Rating Elicitation Strategies for Collaborative Filtering
The accuracy of collaborative filtering recommender systems largely depends on two factors: the quality of the recommendation algorithm and the nature of the available item rating...
Mehdi Elahi, Valdemaras Repsys, Francesco Ricci
SIGIR
2010
ACM
13 years 11 months ago
Temporal diversity in recommender systems
Collaborative Filtering (CF) algorithms, used to build webbased recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current eva...
Neal Lathia, Stephen Hailes, Licia Capra, Xavier A...