Reducing Offline Evaluation Bias in Recommendation Systems release_vjrof7qe4jaufa5bml4rrfl5jq

by Arnaud De Myttenaere, Boris Golden

Released as a article .

2014  

Abstract

Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty of evaluating a recommendation algorithm with historical data (via offline evaluation). This paper analyses this evaluation bias and proposes a simple item weighting solution that reduces its impact. The efficiency of the proposed solution is evaluated on real world data extracted from Viadeo professional social network.
In text/plain format

Archived Files and Locations

application/pdf  360.7 kB
file_uzk5rnunmvf3pkb4ns7iekzgxm
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2014-07-03
Version   v1
Language   en ?
arXiv  1407.0822v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 89ff8caf-5de7-4ed9-8091-66c5e7ceaa22
API URL: JSON