Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience

• URL: http://arxiv.org/abs/2309.14046v1
• Date: Mon, 25 Sep 2023 11:22:19 GMT
• Title: Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience
• Authors: Sangeet Jaiswal, Korah T Malayil, Saif Jawaid, Sreekanth Vempati
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the realm of e-commerce, popular platforms utilize widgets to recommend advertisements and products to their users. However, the prevalence of mobile device usage on these platforms introduces a unique challenge due to the limited screen real estate available. Consequently, the positioning of relevant widgets becomes pivotal in capturing and maintaining customer engagement. Given the restricted screen size of mobile devices, widgets placed at the top of the interface are more prominently displayed and thus attract greater user attention. Conversely, widgets positioned further down the page require users to scroll, resulting in reduced visibility and subsequent lower impression rates. Therefore it becomes imperative to place relevant widgets on top. However, selecting relevant widgets to display is a challenging task as the widgets can be heterogeneous, widgets can be introduced or removed at any given time from the platform. In this work, we model the vertical widget reordering as a contextual multi-arm bandit problem with delayed batch feedback. The objective is to rank the vertical widgets in a personalized manner. We present a two-stage ranking framework that combines contextual bandits with a diversity layer to improve the overall ranking. We demonstrate its effectiveness through offline and online A/B results, conducted on proprietary data from Myntra, a major fashion e-commerce platform in India.

Related papers

• AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents [50.39555842254652]
  We introduce the Android Multi-annotation EXpo (AMEX) to advance research on AI agents in mobile scenarios. AMEX comprises over 104K high-resolution screenshots from 110 popular mobile applications, which are annotated at multiple levels. AMEX includes three levels of annotations: GUI interactive element grounding, GUI screen and element functionality descriptions, and complex natural language instructions.
  arXiv  Detail & Related papers  (2024-07-03T17:59:58Z)
• Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval [85.73149096516543]
  We address the choice of viewpoint during sketch creation in Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) A pilot study highlights the system's struggle when query-sketches differ in viewpoint from target instances. To reconcile this, we advocate for a view-aware system, seamlessly accommodating both view-agnostic and view-specific tasks.
  arXiv  Detail & Related papers  (2024-07-01T21:20:44Z)
• Towards Model-Driven Dashboard Generation for Systems-of-Systems [52.251635013691256]
  This paper describes a model-driven technology-agnostic approach that can automatically transform a simple list of dashboard models into a dashboard model. Dashboard customization can be efficiently obtained by solely modifying the abstract model representation.
  arXiv  Detail & Related papers  (2024-02-23T10:55:18Z)
• Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models [19.10976982327356]
  Mobile Fitting Room is the first on-device diffusion-based virtual try-on system. A usage scenario highlights how our tool can provide a seamless, interactive virtual try-on experience for customers.
  arXiv  Detail & Related papers  (2024-02-02T20:05:45Z)
• Mobile Supply: The Last Piece of Jigsaw of Recommender System [16.56034842580895]
  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.
  arXiv  Detail & Related papers  (2023-08-07T18:06:46Z)
• NiCro: Purely Vision-based, Non-intrusive Cross-Device and Cross-Platform GUI Testing [19.462053492572142]
  We propose a non-intrusive cross-device and cross-platform system NiCro. NiCro uses the state-of-the-art GUI widget detector to detect widgets from GUI images and then analyses a set of comprehensive information to match the widgets across diverse devices. At the system level, NiCro can interact with a virtual device farm and a robotic arm system to perform cross-device, cross-platform testing non-intrusively.
  arXiv  Detail & Related papers  (2023-05-24T01:19:05Z)
• Two-level Data Augmentation for Calibrated Multi-view Detection [51.5746691103591]
  We introduce a new multi-view data augmentation pipeline that preserves alignment among views. We also propose a second level of augmentation applied directly at the scene level. When combined with our simple multi-view detection model, our two-level augmentation pipeline outperforms all existing baselines.
  arXiv  Detail & Related papers  (2022-10-19T17:55:13Z)
• Towards Better Semantic Understanding of Mobile Interfaces [7.756895821262432]
  We release a human-annotated dataset with approximately 500k unique annotations aimed at increasing the understanding of the functionality of UI elements. This dataset augments images and view hierarchies from RICO, a large dataset of mobile UIs. We also release models using image-only and multimodal inputs; we experiment with various architectures and study the benefits of using multimodal inputs on the new dataset.
  arXiv  Detail & Related papers  (2022-10-06T03:48:54Z)
• 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)
• General-Purpose User Embeddings based on Mobile App Usage [46.343844014289246]
  behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users. Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering. We present a tailored AutoEncoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boosting performance.
  arXiv  Detail & Related papers  (2020-05-27T12:01:50Z)

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.