Personalized portals for the wireless user based on mobile agents
WMC '02 Proceedings of the 2nd international workshop on Mobile commerce
Recommender systems using linear classifiers
The Journal of Machine Learning Research
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Using time and activity in personalization for the mobile user
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Scalable architecture for context-aware activity-detecting mobile recommendation systems
WOWMOM '08 Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks
CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Improving Reinforcement Learning by Using Case Based Heuristics
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Dynamically Personalizing Search Results for Mobile Users
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Exploitation and exploration in a performance based contextual advertising system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern based keyword extraction for contextual advertising
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
The wide development of mobile applications provides a considerable amount of data of all types. In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Our work consists in applying machine learning techniques and reasoning process in order to adapt dynamically the MCRS to the evolution of the user's interest. To achieve this goal, we propose to combine bandit algorithm and case-based reasoning in order to define a contextual recommendation process based on different context dimensions (social, temporal and location). This paper describes our ongoing work on the implementation of a MCRS based on a hybrid-ε -greedy algorithm. It also presents preliminary results by comparing the hybrid-ε -greedy and the standard ε -greedy algorithm.