mlsanityData to 14 Oct 2026

Mobile Supply: The Last Piece of Jigsaw of Recommender System

arXiv 2308.03855 ↗First posted 7 Aug 2023cs.IR cs.AIcited by 1

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Abstract (arXiv)

Recommendation system is a fundamental functionality of online platforms. With the development of computing power of mobile phones, some researchers have deployed recommendation algorithms on users' mobile devices to address the problems of data transmission delay and pagination trigger mechanism. However, the existing edge-side mobile rankings cannot completely solve the problem of pagination trigger mechanism. The mobile ranking can only sort the items on the current page, and the fixed set of candidate items limits the performance of the mobile ranking. Besides, after the user has viewed the items of interest to the user on the current page, the user refresh to get a new page of items. This will affect the user's immersive experience because the user is not satisfied with the left items on the current page. In order to address the problem of pagination trigger mechanism, we propose a completely new module in the pipeline of recommender system named Mobile Supply. The pipeline of recommender system is extended to "retrival->pre-ranking->ranking->re-ranking->Mobile Supply->mobile ranking". Specifically, we introduce the concept of list value and use point-wise paradigm to approximate list-wise estimation to calculate the maximum revenue that can be achieved by mobile ranking for the current page. We also design a new mobile ranking approach named device-aware mobile ranking considering the differences of mobile devices tailored to the new pipeline. Extensive offline and online experiments show the superiority of our proposed method and prove that Mobile Supply can further improve the performance of edge-side recommender system and user experience. Mobile Supply has been deployed on the homepage of a large-scale online food platform and has yielded considerable profits in our business.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

18 of 29 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
  3. 2017Gate-Variants of Gated Recurrent Unit (GRU) Neural Networkscited by 242
  4. 2017Beam Search Strategies for Neural Machine Translationcited by 232
  5. 2018Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Ratecited by 173
  6. 2022EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformerscited by 116
  7. 2019Semantic Product Searchcited by 51
  8. 2021Device-Cloud Collaborative Learning for Recommendationcited by 34
  9. 2022Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Modelscited by 33
  10. 2020EdgeRec: Recommender System on Edge in Mobile Taobaocited by 29
  11. 2022Real-time Short Video Recommendation on Mobile Devicescited by 21
  12. 2023Generative Flow Network for Listwise Recommendationcited by 19
6 more
  1. 2019Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performancecited by 13
  2. 2021GRN: Generative Rerank Network for Context-wise Recommendationcited by 13
  3. 2022On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systemscited by 6
  4. 2020DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Networkcited by 5
  5. 2022BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Servicecited by 5
  6. 2023ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraintcited by 3

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