Statistical analysis with missing data
Statistical analysis with missing data
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Integrity constraints: semantics and applications
Logics for databases and information systems
A tutorial on learning with Bayesian networks
Learning in graphical models
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Machine Learning
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Online Recommendation Based on Customer Shopping Model in E-Commerce
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
A Personalized Restaurant Recommender Agent for Mobile E-Service
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Dynamically-optimized context in recommender systems
Proceedings of the 6th international conference on Mobile data management
Contextual recommender problems [extended abstract]
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Enhancing Data Analysis with Noise Removal
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A bayesian metric for evaluating machine learning algorithms
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Exploiting contextual information in recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Online evolutionary context-aware classifier ensemble framework for object recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Context-aware recommendation using rough set model and collaborative filtering
Artificial Intelligence Review
AdPriRec: a context-aware recommender system for user privacy in MANET services
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Bayesian and behavior networks for context-adaptive user interface in a ubiquitous home environment
Expert Systems with Applications: An International Journal
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Acquisition of context poses unique challenges to mobile context-aware recommender systems. The limited resources in these systems make minimizing their context acquisition a practical need, and the uncertainty in the mobile environment makes missing and erroneous context inputs a major concern. In this paper, we propose an approach based on Bayesian networks (BNs) for building recommender systems that minimize context acquisition. Our learning approach iteratively trims the BN-based context model until it contains only the minimal set of context parameters that are important to a user. In addition, we show that a two-tiered context model can effectively capture the causal dependencies among context parameters, enabling a recommender system to compensate for missing and erroneous context inputs. We have validated our proposed techniques on a restaurant recommendation data set and a Web page recommendation data set. In both benchmark problems, the minimal sets of context can be reliably discovered for the specific users. Furthermore, the learned Bayesian network consistently outperforms the J4.8 decision tree in overcoming both missing and erroneous context inputs to generate significantly more accurate predictions.