Affiliation:
1. Shanghai Jiao Tong University, China
2. University of Technology Sydney, Australia
Abstract
When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic
F
airness-
A
ssurance
ST
rategy for s
E
ssion-based
R
ecommender systems (
FASTER
).
FASTER
is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of
FASTER
is verified on three real-world datasets and five original algorithms. The experiment results show that
FASTER
can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.
Funder
Program of Technology Innovation of the Science and Technology Commission of Shanghai Municipality
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
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