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IIR
2010
13 years 9 months ago
Context-Dependent Recommendations with Items Splitting
Recommender systems are intelligent applications that help on-line users to tackle information overload by providing recommendations of relevant items. Collaborative Filtering (CF...
Linas Baltrunas, Francesco Ricci
STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 9 months ago
Probabilistic Association Rules for Item-Based Recommender Systems
Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users ...
Sylvain Castagnos, Armelle Brun, Anne Boyer
GFKL
2007
Springer
196views Data Mining» more  GFKL 2007»
14 years 1 months ago
Comparison of Recommender System Algorithms Focusing on the New-item and User-bias Problem
Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniqu...
Stefan Hauger, Karen H. L. Tso, Lars Schmidt-Thiem...
KDD
2007
ACM
191views Data Mining» more  KDD 2007»
14 years 8 months ago
Modeling relationships at multiple scales to improve accuracy of large recommender systems
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
Robert M. Bell, Yehuda Koren, Chris Volinsky
SOCIALCOM
2010
13 years 5 months ago
A Private and Reliable Recommendation System for Social Networks
Abstract--With the proliferation of internet-based social networks into our lives, new mechanisms to control the release and use of personal data are required. As a step toward thi...
T. Ryan Hoens, Marina Blanton, Nitesh V. Chawla