Agents that reduce work and information overload
Communications of the ACM
A softbot-based interface to the Internet
Communications of the ACM
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Deriving consensus in multiagent systems
Artificial Intelligence
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Machine Learning
Feature-based and Clique-based User Models for Movie Selection: A Comparative Study
User Modeling and User-Adapted Interaction
A Feature-based Approach to Recommending Selections based on Past Preferences
User Modeling and User-Adapted Interaction
Preface to UMUAI Special Issue on MachineLearning for User Modeling
User Modeling and User-Adapted Interaction
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A System for Building Intelligent Agents that Learn to Retrieve and Extract Information
User Modeling and User-Adapted Interaction
Learning to surf: multiagent systems for adaptive web page recommendation
Learning to surf: multiagent systems for adaptive web page recommendation
Interactive Improvisational Music Companionship: A User-Modeling Approach
User Modeling and User-Adapted Interaction
Deploying personalized mobile services in an agent-based environment
Expert Systems with Applications: An International Journal
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Using Evolving Agents to Critique Subjective Music Compositions
Computational Intelligence and Security
A platform for virtual museums with personalized content
Multimedia Tools and Applications
Personalized movie recommendation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Personalizing internet information services: passive filtering and active retrieval
International Journal of Computers and Applications
Personalization of content in virtual exhibitions
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Evaluating subjective compositions by the cooperation between human and adaptive agents
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Vizier: a generic and multidimensional agent-based recommendation framework
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Estimating importance of implicit factors in e-commerce recommender systems
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
| Hi-index | 0.00 |
Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.