Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2nd international conference on Knowledge capture
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
PHASES: A User Profile Learning Approach for Web Search
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Uncertainty Issues and Algorithms in Automating Process Connecting Web and User
Uncertainty Reasoning for the Semantic Web I
UPComp - A PHP Component for Recommendation Based on User Behaviour
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Evaluating top-k algorithms with various sources of data and user preferences
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Estimating importance of implicit factors in e-commerce recommender systems
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
User feedback and preferences mining
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Hi-index | 0.00 |
In this paper we present a proposal of a system that combines various methods of user modelling. This system may find its application in e-commerce, recommender systems, etc. The main focus of this paper is on automatic methods that require only a small amount of data from user. The different ways of integration of user models are studied. A proof-of-concept implementation is compared to standard methods in an initial experiment with artificial user data...