Evaluating recommender behavior for new users

Daniel Kluver, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Scopus citations

Abstract

The new user experience is one of the important problems in recommender systems. Past work on recommending for new users has focused on the process of gathering information from the user. Our work focuses on how different algorithms behave for new users. We describe a methodology that we use to compare representatives of three common families of algorithms along eleven different metrics. We find that for the first few ratings a baseline algorithm performs better than three common collaborative filtering algorithms. Once we have a few ratings, we find that Funk's SVD algorithm has the best overall performance. We also find that ItemItem, a very commonly deployed algorithm, performs very poorly for new users. Our results can inform the design of interfaces and algorithms for new users.

Original languageEnglish (US)
Title of host publicationRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages121-128
Number of pages8
ISBN (Electronic)9781450326681
DOIs
StatePublished - Oct 6 2014
Event8th ACM Conference on Recommender Systems, RecSys 2014 - Foster City, United States
Duration: Oct 6 2014Oct 10 2014

Publication series

NameRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems

Other

Other8th ACM Conference on Recommender Systems, RecSys 2014
CountryUnited States
CityFoster City
Period10/6/1410/10/14

Keywords

  • Evaluation
  • New user experience
  • New user problem
  • Profile size
  • Recommender systems
  • User cold start

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