Details
| Original language | English |
|---|---|
| Title of host publication | The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 101-102 |
| Number of pages | 2 |
| ISBN (electronic) | 9781450356404 |
| Publication status | Published - 2018 |
| Event | 27th International World Wide Web, WWW 2018 - Lyon, France Duration: 23 Apr 2018 → 27 Apr 2018 |
Abstract
Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.
Keywords
- diversity, fairness, recommender systems, user satisfaction
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018. Association for Computing Machinery (ACM), 2018. p. 101-102.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - User Fairness in Recommender Systems
AU - Leonhardt, Jurek
AU - Anand, Avishek
AU - Khosla, Megha
N1 - Funding information: This work is partially funded by ALEXANDRIA (ERC 339233).
PY - 2018
Y1 - 2018
N2 - Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.
AB - Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.
KW - diversity
KW - fairness
KW - recommender systems
KW - user satisfaction
UR - http://www.scopus.com/inward/record.url?scp=85084188085&partnerID=8YFLogxK
U2 - 10.1145/3184558.3186949
DO - 10.1145/3184558.3186949
M3 - Conference contribution
AN - SCOPUS:85084188085
SP - 101
EP - 102
BT - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PB - Association for Computing Machinery (ACM)
T2 - 27th International World Wide Web, WWW 2018
Y2 - 23 April 2018 through 27 April 2018
ER -