Similarity of users' (content-based) preference models for Collaborative filtering in few ratings scenario

Authors:
Alan Eckhardt
Affiliations:
Department of Software Engineering, Charles University in Prague, Prague, Czech Republic
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 13
Cited 2

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
A Framework for Collaborative, Content-Based and Demographic Filtering

Artificial Intelligence Review - Special issue on data mining on the Internet
Getting to know you: learning new user preferences in recommender systems

Proceedings of the 7th international conference on Intelligent user interfaces
Eigentaste: A Constant Time Collaborative Filtering Algorithm

Information Retrieval
User Preference Mining through Collaborative Filtering and Content Based Filtering in Recommender System

EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Content-boosted collaborative filtering for improved recommendations

Eighteenth national conference on Artificial intelligence
Nantonac collaborative filtering: recommendation based on order responses

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Unifying collaborative and content-based filtering

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Considering Data-Mining Techniques in User Preference Learning

WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Regression-based latent factor models

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Similarity Measure for Collaborative Filtering with Implicit Feedback

ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Pref Shop A Web Shop with User Preference Search Capabilities

WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03

A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes

Information Processing and Management: an International Journal
Negative implicit feedback in e-commerce recommender systems

Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics

Quantified Score

Hi-index	12.05

Visualization

Abstract

Collaborative filtering is an efficient way to find best objects to recommend. This technique is particularly useful when there is a lot of users that rated a lot of objects. In this paper, we propose a method that improve the Collaborative filtering in situations, where the number of ratings or users is small. The proposed approach is experimentally evaluated on real datasets with very convincing results.