Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Decision Rules, Bayes' Rule and Ruogh Sets
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
User Modeling and User-Adapted Interaction
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
Evaluating the effectiveness of explanations for recommender systems
User Modeling and User-Adapted Interaction
Hi-index | 0.00 |
Conflict resolution in context-aware computing is getting more significant attention from researchers as pervasive/ubiquitous computing environments take into account multiple users and multiple applications. In multi-user ubiquitous computing environments, conflicts among user's contexts need to be detected and resolved. Conflicts arise when multiple users try to access or try to have a control on an application. In this paper, the authors propose a series of algorithms to resolve conflict which can be embedded in different context aware applications like context aware devices (say TV, Mobile, AC, and Fan) and Context Aware Ambient (like Meeting Room, Living Room, Restaurant, Coffee Shop, etc.). The algorithms discussed in this paper make use of different tools like Probability, Fuzzy Logic, Bayesian Network and Rough set theory. In addition the algorithms utilize various factors like social, personal and environmental. The motto of this paper is to enable context aware applications to offer socialized and personalized services to multiple users by resolving service conflicts among users.