Multi-Task Learning for User Engagement and Adoption in Live Video Streaming Events

• URL: http://arxiv.org/abs/2106.10305v1
• Date: Fri, 18 Jun 2021 18:30:22 GMT
• Title: Multi-Task Learning for User Engagement and Adoption in Live Video Streaming Events
• Authors: Stefanos Antaris and Dimitrios Rafailidis and Romina Arriaza
• Abstract summary: We present a multi-task deep reinforcement learning model to select the time of a live video streaming event. We consider the engagement and adoption of the viewers as independent tasks and formulate a unified loss function to learn a common policy. Our experiments demonstrate the effectiveness of the proposed model when compared with several state-of-the-art strategies.
• Score: 7.5413579967970605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 multi-task 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.

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