Interest Clock: Time Perception in Real-Time Streaming Recommendation System
@article{Zhu2024InterestCT, title={Interest Clock: Time Perception in Real-Time Streaming Recommendation System}, author={Yongchun Zhu and Jingwu Chen and Ling Chen and Yitan Li and Feng Zhang and Zuotao Liu}, journal={ArXiv}, year={2024}, volume={abs/2404.19357}, url={https://api.semanticscholar.org/CorpusID:269457305} }
This paper proposes an effective and universal method Interest Clock to perceive time information in recommendation systems, which encodes users' time-aware preferences into a clock 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.
5 Citations
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12 References
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
- 2023
Computer Science
This work proposes TWo-stage Interest Network (TWIN), where the Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins.
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
- 2020
Computer Science
A new cascaded search paradigm enables SIM with a better ability to model lifelong sequential behavior data in both scalability and accuracy, and also introduces the hands-on experience on how to implement SIM in large scale industrial systems.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
- 2018
Computer Science
A Convolutional Sequence Embedding Recommendation Model »Caser» is proposed, which is to embed a sequence of recent items into an image in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters.
User Consumption Intention Prediction in Meituan
- 2021
Computer Science, Business
In Meituan, a real-world system consisting of two stages, intention detection and prediction, a graph neural network-based intention prediction model GRIP is designed, which can capture user intrinsic preference and spatio-temporal context.
Deep Interest Network for Click-Through Rate Prediction
- 2018
Computer Science
A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
- 2023
Computer Science
This work designs a dual interaction-aware module, aiming to capture users' dual preferences based on their interactions with stores and foods, and demonstrates that this model outperforms state-of-the-art methods on real-world datasets and is capable of modeling the dual period-varying preferences.
Collaborative Knowledge Base Embedding for Recommender Systems
- 2016
Computer Science
A heterogeneous network embedding method is adopted, termed as TransR, to extract items' structural representations by considering the heterogeneity of both nodes and relationships and a final integrated framework, which is termed as Collaborative Knowledge Base Embedding (CKE), to jointly learn the latent representations in collaborative filtering.
Automatically Discovering User Consumption Intents in Meituan
- 2022
Computer Science, Business
This work designs the AutoIntent system, consisting of the disentangled intent encoder and intent discovery decoder, and proposes to build intent-pair pseudo labels based on the denoised feature similarities to transfer knowledge from known intents to new ones.
Deep Neural Networks for YouTube Recommendations
- 2016
Computer Science
This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
- 2021
Computer Science
This work proposes an improved framework DCN-V2, which is simple, can be easily adopted as building blocks, and has delivered significant offline accuracy and online business metrics gains across many web-scale learning to rank systems at Google.