The Technologies Used for Artwork Personalization and the Challenges

DOI

10.2991/978-94-6463-124-1_28How to use a DOI?

Keywords

Artwork personalization; contextual bandits approach; A/B testing; predictive data analysis

Abstract

For many years, many streaming companies’ personalized recommendation systems mainly focused on how to employ the right algorithm to predict what the subscriber would be interested in based on their viewing history and preferences. By showing those content, companies believe this could provide efficiency to their subscribers and thus their content could reach better performance. However, since it is impossible to present all the details of that content on the homepage, the title shown is unable to contain enough information to trigger the user to click on that. Instead, the artwork which represents the content plays a significant role in the number of clicks the specific content could receive. Although few companies already realized this unprecedented aspect of the personalized recommendation system and started to work on the development of a certain algorithm using A/B testing and contextual bandits approach to improve the system, there are limited research methods that have been employed and they are still facing many challenges. By examining how the A/B testing and contextual bandits approach actually work and the logic behind these two basic research tools and at the same time dealing with those challenges, companies could come up with more comprehensive research designs to better study the users’ reactions and inclination when provided with different artworks. Thus, fully understanding where those challenges lay could help companies to be sure about the future development direction of the personalized recommendation field.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhuoyan Guo
PY  - 2023
DA  - 2023/03/29
TI  - The Technologies Used for Artwork Personalization and the Challenges
BT  - Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022)
PB  - Atlantis Press
SP  - 230
EP  - 238
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-124-1_28
DO  - 10.2991/978-94-6463-124-1_28
ID  - Guo2023
ER  -