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» Learning and adaptivity in interactive recommender systems
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WWW
2010
ACM
14 years 2 months ago
Factorizing personalized Markov chains for next-basket recommendation
Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn...
Steffen Rendle, Christoph Freudenthaler, Lars Schm...
DL
1999
Springer
111views Digital Library» more  DL 1999»
13 years 11 months ago
TalkMine and the Adaptive Recommendation Project
TalkMine is an adaptive recommendation system which is both content-based and collaborative, and further allows the crossover of information among multiple databases searched by u...
Luis Mateus Rocha
ICML
2000
IEEE
14 years 8 months ago
Learning Subjective Functions with Large Margins
In manyoptimization and decision problems the objective function can be expressed as a linear combinationof competingcriteria, the weights of whichspecify the relative importanceo...
Claude-Nicolas Fiechter, Seth Rogers
HIS
2004
13 years 8 months ago
A Case-Based Recommender for Task Assignment in Heterogeneous Computing Systems
Case-based reasoning (CBR) is a knowledge-based problem-solving technique, which is based on reuse of previous experiences. In this paper we propose a new model for static task as...
S. Ghanbari, Mohammad Reza Meybodi, Kambiz Badie
ADAPTIVE
2007
Springer
14 years 1 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...