Paper Info
Title
Controlling Consistency in Top-N Recommender Systems
Abstract
Recommender systems have become essential navigational tools for users to surf through vast on-line catalogs. However, recommender algorithms are often tuned to improve accuracy, without paying any attention to the consistency of the recommendations when small changes happen to the user profile or to the model. Consistency of recommendations is closely related with user satisfaction and trust. In this work we analyze how small changes in either the user profile or the recommender model may affect the consistency of Top-N recommendation systems. We also design two mechanisms able to promote consistency without degrading accuracy and novelty of recommendations. Finally, we investigate the consistency of Top-N recommendation algorithms over time by analyzing real data from a production IPTV recommender system.
Year
DOI
Venue
2010
10.1109/ICDMW.2010.65
ICDM Workshops
Keywords
Field
DocType
recommender system,degrading accuracy,recommender model,recommender algorithm,user profile,top-n recommendation system,user satisfaction,controlling consistency,production iptv recommender system,top-n recommendation algorithm,small change,top-n recommender systems,tv,consistency,noise measurement,algorithm design and analysis,motion pictures,recommender systems,recall
Recommender system,User profile,Algorithm design,Information retrieval,Computer science,Artificial intelligence,Navigational instrument,Novelty,IPTV,Machine learning
Conference
Citations 
PageRank 
References 
2
0.36
15
Authors
2
Name
Order
Citations
PageRank
Paolo Cremonesi1130687.23
Roberto Turrin285934.94