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View all- Ma XZhou QLi Y(2024)Multi-interest sequential recommendation with contrastive learning and temporal analysisKnowledge-Based Systems10.1016/j.knosys.2024.112657305(112657)Online publication date: Dec-2024
Interest Clock: Time Perception in Real-Time Streaming Recommendation System
Authors: 
Yongchun Zhu, Jingwu Chen, Ling Chen, Yitan Li, Feng Zhang, Zuotao LiuAuthors Info & Claims
Yongchun Zhu, Jingwu Chen, Ling Chen, Yitan Li, Feng Zhang, Zuotao LiuAuthors Info & ClaimsPages 2915 - 2919
Abstract
User preferences follow a dynamic pattern over a day, e.g., at 8 am, a user might prefer to read news, while at 8 pm, they might prefer to watch movies. Time modeling aims to enable recommendation systems to perceive time changes to capture users' dynamic preferences over time, which is an important and challenging problem in recommendation systems. Especially, streaming recommendation systems in the industry, with only available samples of the current moment, present greater challenges for time modeling. There is still a lack of effective time modeling methods for streaming recommendation systems. In this paper, we propose an effective and universal method Interest Clock to perceive time information in recommendation systems. Interest Clock first encodes users' time-aware preferences into a clock (hour-level personalized features) and then uses Gaussian distribution to smooth and aggregate them into the final interest clock embedding according to the current time for the final prediction. By arming base models with Interest Clock, we conduct online A/B tests, obtaining +0.509% and +0.758% improvements on user active days and app duration respectively. Besides, the extended offline experiments show improvements as well. Interest Clock has been deployed on Douyin Music App.
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- Interest Clock: Time Perception in Real-Time Streaming Recommendation System
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Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
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Published: 11 July 2024
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SIGIR 2024
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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
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View all- Ma XZhou QLi Y(2024)Multi-interest sequential recommendation with contrastive learning and temporal analysisKnowledge-Based Systems10.1016/j.knosys.2024.112657305(112657)Online publication date: Dec-2024
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