Algorithms for clustering data
Algorithms for clustering data
Vector quantization and signal compression
Vector quantization and signal compression
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Experimental results of randomized clustering algorithm
Proceedings of the twelfth annual symposium on Computational geometry
Fab: content-based, collaborative recommendation
Communications of the ACM
Exact and approximation algorithms for clustering
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
User needs for location-aware mobile services
Personal and Ubiquitous Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Context-aware system for proactive personalized service based on context history
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Due to exponential growth of available information about products, users face problems when trying to identify products of interest. Because of this there is a need to design systems to help users select products more tailored to their interests through recognition of users' behavior. In the present paper, by using data mining techniques and making clustering model, the users with similar preferences were recognized and provided with appropriate recommendations. These recommendations were not only personalized on the basis of customer's behavior but also by considering user's location. MovieLens, a movie recommendation site, was used for model assessment. The results of the assessment showed that the method presented in this paper has a good accuracy for prediction of users' preferences.