Ajou University repository

FPAdaMetric: False-Positive-Aware Adaptive Metric Learning for Session-Based Recommendationoa mark
Citations

SCOPUS

0

Citation Export

Publication Year
2022-06-30
Journal
Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Vol.36, pp.4039-4047
Mesh Keyword
Adaptive metricsAdaptive modulesDeleterious effectsFalse positiveImplicit feedbackMetric learningMETRIC modelPositive constraintsRecommendation algorithmsUser behaviors
All Science Classification Codes (ASJC)
Artificial Intelligence
Abstract
Modern recommendation systems are mostly based on implicit feedback data which can be quite noisy due to false positives (FPs) caused by many reasons, such as misclicks or quick curiosity. Numerous recommendation algorithms based on collaborative filtering have leveraged post-click user behavior (e.g., skip) to identify false positives. They effectively involved these false positives in the model supervision as negative-like signals. Yet, false positives had not been considered in existing session-based recommendation systems (SBRs) although they provide just as deleterious effects. To resolve false positives in SBRs, we first introduce FP-Metric model which reformulates the objective of the session-based recommendation with FP constraints into metric learning regularization. In addition, we propose FP-AdaMetric that enhances the metric-learning regularization terms with an adaptive module that elaborately calculates the impact of FPs inside sequential patterns. We verify that FP-AdaMetric improves several session-based recommendation models' performances in terms of Hit Rate (HR), MRR, and NDCG on datasets from different domains including music, movie, and game. Furthermore, we show that the adaptive module plays a much more crucial role in FP-AdaMetric model than in other baselines.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36846
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142385885&origin=inward
DOI
https://doi.org/10.1609/aaai.v36i4.20321
Journal URL
https://aaai.org/Library/AAAI/aaai22contents.php
Type
Conference
Funding
We appreciate Hoyeop Lee from NCSOFT, Seongwon Jang from TMAP Mobility, and anonymous reviewers for the great discussion about our work.
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Cho, Hyunsouk Image
Cho, Hyunsouk조현석
Department of Software and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.