270 search results - page 39 / 54 » Explaining collaborative filtering recommendations |
SIGIR
2003
ACM
14 years 1 months ago
2003
ACM
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this ...
AAAI
2010
13 years 9 months ago
2010
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...
KDD
2009
ACM
14 years 2 months ago
2009
ACM
One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender system settings. Typical characteristics include: Only positive examples can be observed, ...
RECSYS
2009
ACM
14 years 2 months ago
2009
ACM
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users’ ratings for items. It assumes that the users’ previously recorded ratin...
SDM
2010
SIAM
13 years 9 months ago
2010
SIAM
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, ...