GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
A time-based approach to effective recommender systems using implicit feedback
Expert Systems with Applications: An International Journal
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Regularized Linear Models in Stacked Generalization
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Stacking recommendation engines with additional meta-features
Proceedings of the third ACM conference on Recommender systems
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Predicting most rated items in Weekly Recommendation with temporal regression
Proceedings of the Workshop on Context-Aware Movie Recommendation
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An approach to social recommendation for context-aware mobile services
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Group recommendation in context
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Signal-based user recommendation on twitter
Proceedings of the 22nd international conference on World Wide Web companion
| Hi-index | 0.00 |
Most of the existing recommendation engines do not take into consideration contextual information for suggesting interesting items to users. Features such as time, location, or weather, may affect the user preferences for a particular item. In this paper, we propose two different context-aware approaches for the movie recommendation task. The first is an hybrid recommender that assesses available contextual factors related to time in order to increase the performance of traditional CF approaches. The second approach aims at identifying users in a household that submitted a given rating. This latter approach is based on machine learning techniques, namely, neural networks and majority voting classifiers. The effectiveness of both the approaches has been experimentally validated using several evaluation metrics and a large dataset.