An effective model-free Gaussian Process based online social media recommendation

Jiawei Xu, Yufeng Wang*, Jianhua Ma, Qun Jin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, a variety of social media platforms acts as the major information portals, frequently updates the news and media pool and recommends them to dynamically arriving individuals, which requires real-time performance and immediate response to user feedback. Traditional offline/static recommendation approaches assume there exist large amount of historical interactive data, and build models from these data, which can neither deal with cold-start issue and nor fully incorporate individuals' feedbacks. Reinforcement learning based online recommendation system such as multi-armed bandit (MAB) and contextual MAB, attempts to maximize the long-term learning gains over time through exploring and exploiting the inherently interactive nature of online learning processes. However, most of work adopt the model-based paradigm, in which the feedback/reward function of individual is statically set as a specific format (e.g., linear in the features of the recommended contents and individual), which can only tailor to some specific learning objective and individual model. Without assuming the fixed reward model for each individual, this paper utilizes Gaussian process (GP) to characterize the expected feedback/reward of any recommended social media. Then, targeting at relatively accurate and noisy feedbacks of individual users, two MAB based algorithms are designed to select recommended social media to balance the effect of exploration and exploitation. The experimental results on real social media dataset Movielens demonstrate the effectiveness of our proposed methods compared with other model-based recommendation schemes.

Original languageEnglish
Title of host publicationProceedings - IEEE Congress on Cybermatics
Subtitle of host publication2022 IEEE International Conferences on Internet of Things, iThings 2022, IEEE Green Computing and Communications, GreenCom 2022, IEEE Cyber, Physical and Social Computing, CPSCom 2022 and IEEE Smart Data, SmartData 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-378
Number of pages5
ISBN (Electronic)9781665454179
DOIs
Publication statusPublished - 2022
Event2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022 - Espoo, Finland
Duration: 2022 Aug 222022 Aug 25

Publication series

NameProceedings - IEEE Congress on Cybermatics: 2022 IEEE International Conferences on Internet of Things, iThings 2022, IEEE Green Computing and Communications, GreenCom 2022, IEEE Cyber, Physical and Social Computing, CPSCom 2022 and IEEE Smart Data, SmartData 2022

Conference

Conference2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022
Country/TerritoryFinland
CityEspoo
Period22/8/2222/8/25

Keywords

  • Gaussian Process
  • Multi-Armed Bandit
  • Online Recommendation
  • Reinforcement Learning
  • Social media

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization
  • Communication
  • Computer Science Applications

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