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
Optimization of inverted vector searches
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Database Systems (TODS)
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
File Structures: An Object-Oriented Approach with C++
File Structures: An Object-Oriented Approach with C++
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collaborative Learning and Recommender Systems
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Text retrieval methods for item ranking in collaborative filtering
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Using profile expansion techniques to alleviate the new user problem
Information Processing and Management: an International Journal
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
Using rating matrix compression techniques to speed up collaborative recommendations
Information Retrieval
Bridging memory-based collaborative filtering and text retrieval
Information Retrieval
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This paper explores the possibility of using a disk based inverted file structure for collaborative filtering. Our hypothesis is that this allows for faster calculation of predictions and also that early termination heuristics may be used to further speed up the filtering process and perhaps even improve the quality of the predictions. In an experiment on the EachMovie dataset this was tested. Our results indicate that searching the inverted file structure is many times faster than general in-memory vector search, even for very large profiles. The Continue termination heuristics produces the best ranked predictions in our experiments, and Quit is the top performer in terms of speed.