Mobile Supply: The Last Piece of Jigsaw of Recommender System

• URL: http://arxiv.org/abs/2308.03855v2
• Date: Wed, 9 Aug 2023 02:36:50 GMT
• Title: Mobile Supply: The Last Piece of Jigsaw of Recommender System
• Authors: Zhenhao Jiang, Biao Zeng, Hao Feng, Jin Liu, Jie Zhang, Jia Jia, Ning Hu
• Abstract summary: We propose a completely new module in the pipeline of recommender system named Mobile Supply->mobile ranking. We use point-wise paradigm to approximate list-wise estimation to the 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.
• Score: 16.56034842580895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception [52.5831204440714]
  We introduce Mobile-Agent, an autonomous multi-modal mobile device agent. Mobile-Agent first leverages visual perception tools to accurately identify and locate both the visual and textual elements within the app's front-end interface. It then autonomously plans and decomposes the complex operation task, and navigates the mobile Apps through operations step by step.
  arXiv  Detail & Related papers  (2024-01-29T13:46:37Z)
• Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience [0.0]
  Given the restricted screen size of mobile devices, widgets placed at the top of the interface are more prominently displayed. We model the vertical widget reordering as a contextual multi-arm bandit problem with delayed batch feedback. We present a two-stage ranking framework that combines contextual bandits with a diversity layer to improve the overall ranking.
  arXiv  Detail & Related papers  (2023-09-25T11:22:19Z)
• Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation [14.9065245548275]
  We propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. Specifically, we introduce four different types of page-level feedback as input, and capture user preference for item arrangement under different receptive fields.
  arXiv  Detail & Related papers  (2022-04-01T11:58:00Z)
• PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation [48.17295872384401]
  We present a personalized re-ranking model (dubbed PEAR) based on contextualized transformer. PEAR makes several major improvements over the existing methods. We also augment the training of PEAR with a list-level classification task to assess users' satisfaction on the whole ranking list.
  arXiv  Detail & Related papers  (2022-03-23T08:29:46Z)
• Online Learning of Optimally Diverse Rankings [63.62764375279861]
  We propose an algorithm that efficiently learns the optimal list based on users' feedback only. We show that after $T$ queries, the regret of LDR scales as $O((N-L)log(T))$ where $N$ is the number of all items.
  arXiv  Detail & Related papers  (2021-09-13T12:13:20Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants [42.25496752260081]
  This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users.
  arXiv  Detail & Related papers  (2021-01-09T17:07:47Z)
• A Real-Time Whole Page Personalization Framework for E-Commerce [13.254747746069139]
  E-commerce platforms contain multiple carousels on their homepage. Items within a carousel may change dynamically based on sequential user actions. We present a scalable end-to-end production system to optimally rank item-carousels in real-time on the Walmart online grocery homepage.
  arXiv  Detail & Related papers  (2020-12-08T19:08:41Z)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)
• Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation [24.810164687987243]
  Higher positions lead to more clicks for one commodity. Only a few recommended items are shown at first glance and users need to slide the screen to browse other items. Some recommended items ranked behind are not viewed by users and it is not proper to treat this kind of items as negative samples.
  arXiv  Detail & Related papers  (2020-08-21T08:22:30Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.