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KDD
2005
ACM
163views Data Mining» more  KDD 2005»
12 years 5 months ago
Data Sparsity Issues in the Collaborative Filtering Framework
Abstract. With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user effici...
Miha Grcar, Dunja Mladenic, Blaz Fortuna, Marko Gr...
AAAI
2010
11 years 6 months ago
Transfer Learning in Collaborative Filtering for Sparsity Reduction
Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are...
Weike Pan, Evan Wei Xiang, Nathan Nan Liu, Qiang Y...
SIGIR
2006
ACM
11 years 11 months ago
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large ...
Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
ITRUST
2005
Springer
11 years 10 months ago
Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences
Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar” according to their logg...
Manos Papagelis, Dimitris Plexousakis, Themistokli...
KDD
2009
ACM
298views Data Mining» more  KDD 2009»
12 years 2 days ago
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, ...
Rong Pan, Martin Scholz
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