Context-based splitting of item ratings in collaborative filtering
L Baltrunas, F Ricci - Proceedings of the third ACM conference on …, 2009 - dl.acm.org
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data
set of users' ratings for items. It assumes that the users' previously recorded ratings can help …
set of users' ratings for items. It assumes that the users' previously recorded ratings can help …
[PDF][PDF] Context-dependent items generation in collaborative filtering
L Baltrunas, F Ricci - Proceedings of the 2009 Workshop on …, 2009 - researchgate.net
Collaborative Filtering (CF) exploits users' recorded ratings for predicting ratings on items
not evaluated yet. In classical CF each item is modelled by a set of users' ratings not …
not evaluated yet. In classical CF each item is modelled by a set of users' ratings not …
Experimental evaluation of context-dependent collaborative filtering using item splitting
L Baltrunas, F Ricci - User Modeling and User-Adapted Interaction, 2014 - Springer
Collaborative Filtering (CF) computes recommendations by leveraging a historical data set
of users' ratings for items. CF assumes that the users' recorded ratings can help in predicting …
of users' ratings for items. CF assumes that the users' recorded ratings can help in predicting …
Cross-domain collaborative filtering over time
Collaborative filtering (CF) techniques recommend items to users based on their historical
ratings. In real-world scenarios, user interests may drift over time since they are affected by …
ratings. In real-world scenarios, user interests may drift over time since they are affected by …
CCCF: Improving collaborative filtering via scalable user-item co-clustering
Collaborative Filtering (CF) is the most popular method for recommender systems. The
principal idea of CF is that users might be interested in items that are favorited by similar …
principal idea of CF is that users might be interested in items that are favorited by similar …
Listwise collaborative filtering
Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great
success in recommender systems. They obtained state-of-the-art performances by …
success in recommender systems. They obtained state-of-the-art performances by …
Beyond collaborative filtering: The list recommendation problem
O Sar Shalom, N Koenigstein, U Paquet… - Proceedings of the 25th …, 2016 - dl.acm.org
Most Collaborative Filtering (CF) algorithms are optimized using a dataset of isolated user-
item tuples. However, in commercial applications recommended items are usually served as …
item tuples. However, in commercial applications recommended items are usually served as …
Clustering-and regression-based multi-criteria collaborative filtering with incremental updates
Recommender systems are a valuable means for online users to find items of interest in
situations when there exists a large set of alternatives. Collaborative Filtering (CF) is a …
situations when there exists a large set of alternatives. Collaborative Filtering (CF) is a …
Advances in collaborative filtering
Collaborative filtering (CF) methods produce recommendations based on usage patterns
without the need of exogenous information about items or users. CF algorithms have shown …
without the need of exogenous information about items or users. CF algorithms have shown …
Online learning for collaborative filtering
Collaborative filtering (CF), aiming at predicting users' unknown preferences based on
observational preferences from some users, has become one of the most successful …
observational preferences from some users, has become one of the most successful …