Neural networks and the bias/variance dilemma
Neural Computation
Learning users' interests by unobtrusively observing their normal behavior
Proceedings of the 5th international conference on Intelligent user interfaces
Proceedings of the 6th international conference on Intelligent user interfaces
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
The determinants of web page viewing behavior: an eye-tracking study
Proceedings of the 2004 symposium on Eye tracking research & applications
A nonparametric hierarchical bayesian framework for information filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
An implicit feedback approach for interactive information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Combining multiple forms of evidence while filtering
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized interactive faceted search
Proceedings of the 17th international conference on World Wide Web
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
Dynamically constructing user profiles with similarity-based online incremental clustering
International Journal of Advanced Intelligence Paradigms
Evaluating Interface Variants on Personality Acquisition for Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Hit me baby one more time: a haptic rating interface
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
Web user behavioral profiling for user identification
Decision Support Systems
A framework analysis for managing explicit feedback of visitors of a web site
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
An Investigation of User Behaviour Consistency for Context-Aware Information Retrieval Systems
International Journal of Advanced Pervasive and Ubiquitous Computing
| Hi-index | 0.00 |
Research in information retrieval is now moving into a personalized scenario where a retrieval or filtering system maintains a separate user profile for each user. In this framework, information delivered to the user can be automatically personalized and catered to individual user's information needs. However, a practical concern for such a personalized system is the "cold start problem": any user new to the system must endure poor initial performance until sufficient feedback from that user is provided.To solve this problem, we use both explicit and implicit feedback to build a user's profile and use Bayesian hierarchical methods to borrow information from existing users. We analyze the usefulness of implicit feedback and the adaptive performance of the model on two data sets gathered from user studies where users' interaction with a document, or implicit feedback, were recorded along with explicit feedback. Our results are two-fold: first, we demonstrate that the Bayesian modeling approach effectively trades off between shared and user-specific information, alleviating poor initial performance for each user. Second, we find that implicit feedback has very limited unstable predictive value by itself and only marginal value when combined with explicit feedback.