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Multi-task Learning for User Engagement and Adoption in Live Video Streaming Events
KTH. Hive Streaming AB, Stockholm, Sweden..ORCID iD: 0000-0002-1135-8863
Univ Thessaly, Volos, Greece..
Hive Streaming AB, Stockholm, Sweden..
2021 (English)In: MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: APPLIED DATA SCIENCE TRACK, PT V / [ed] Dong, Y Kourtellis, N Hammer, B Lozano, JA, Springer Nature , 2021, Vol. 12979, p. 463-478Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, live video streaming events have become a mainstay in viewer's communication in large international enterprises. Provided that viewers are distributed worldwide, the main challenge resides on how to schedule the optimal event's time so as to improve both the viewer's engagement and adoption. In this paper we present a multitask deep reinforcement learning model to select the time of a live video streaming event, aiming to optimize the viewer's engagement and adoption at the same time. We consider the engagement and adoption of the viewers as independent tasks and formulate a unified loss function to learn a common policy. In addition, we account for the fact that each task might have different contribution to the training strategy of the agent. Therefore, to determine the contribution of each task to the agent's training, we design a Transformer's architecture for the state-action transitions of each task. We evaluate our proposed model on four real-world datasets, generated by the live video streaming events of four large enterprises spanning from January 2019 until March 2021. Our experiments demonstrate the effectiveness of the proposed model when compared with several state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/merlin.

Place, publisher, year, edition, pages
Springer Nature , 2021. Vol. 12979, p. 463-478
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743
Keywords [en]
Multi-task learning, Reinforcement learning, Live video streaming
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-305535DOI: 10.1007/978-3-030-86517-7_29ISI: 000713051100029Scopus ID: 2-s2.0-85115702748OAI: oai:DiVA.org:kth-305535DiVA, id: diva2:1620344
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), SEP 13-17, 2021, ELECTR NETWORK
Note

QC 20211215

conference ISBN 978-3-030-86517-7; 978-3-030-86516-0

Available from: 2021-12-15 Created: 2021-12-15 Last updated: 2022-06-25Bibliographically approved

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Antaris, Stefanos

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