Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Self-organizing maps
Collecting user access patterns for building user profiles and collaborative filtering
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
WCML: paving the way for reuse in object-oriented Web engineering
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 2
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Construction of Adaptive Web-Applications from Reusable Components
EC-WEB '00 Proceedings of the First International Conference on Electronic Commerce and Web Technologies
Semantic Navigation Maps for Information Agents
CIA '98 Proceedings of the Second International Workshop on Cooperative Information Agents II, Learning, Mobility and Electronic Commerce for Information Discovery on the Internet
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An enhanced ART2 neural network for clustering analysis
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
International Journal of Parallel, Emergent and Distributed Systems
| Hi-index | 0.00 |
Since the World Wide Web has become widespread, more and more applications exist that are suitable for the application of social information filtering techniques. In collaborative filtering, preferences of a user are estimated through mining data available about the whole user population, implicitly exploiting analogies between users that show similar characteristics. These preferences are then normally used to filter content or functionality of an application. Two important factors for the quality of the filtering process are the number of users and the amount of information (such as observed behaviors) available about each user. Another factor is the number of objects in the pool of the application that can be considered during the filtering process. Today in most cases memory based approaches to collaborative filtering are used. Unfortunately with O(#users * #items) those do not scale well. Therefore we implemented a model based approach using two different types of neural networks and benchmarked them against a widely used memory based approach. Especially with ART2 networks we obtained some encouraging results.