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traffic allocation

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DataFunSummit
DataFunSummit
Jan 5, 2025 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

The article presents a CIKM‑2024 paper that introduces MODRL‑TA, a multi‑objective deep reinforcement learning system combining multi‑objective Q‑learning, a cross‑entropy‑based decision‑fusion algorithm, and a progressive data‑augmentation pipeline to dynamically allocate search traffic on JD.com, with both offline and online experiments showing substantial gains in CTR, CVR, and overall platform performance.

cross-entropy methoddeep learninge-commerce
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
JD Retail Technology
JD Retail Technology
Dec 26, 2024 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

MODRL‑TA is a multi‑objective deep reinforcement learning framework that unites independent Q‑learning agents, a cross‑entropy‑based decision‑fusion module, and progressive data‑augmentation to overcome cold‑start and multi‑objective trade‑offs in e‑commerce traffic allocation, delivering up to 18% more impressions, 4% higher CTR and 5% higher CVR in live tests.

deep learninge-commercemulti-objective
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
DataFunTalk
DataFunTalk
Feb 24, 2023 · Artificial Intelligence

Designing Experiments for Two‑Sided Advertising Markets

This article explains the challenges of A/B testing in two‑sided advertising markets and presents several experimental designs—including four‑cell traffic experiments, counterfactual interleaving, joint sampling, and simulation systems—illustrated with Tencent’s practical implementations to mitigate interference, spillover, and competition effects.

AB testingad experimentsadvertising
0 likes · 15 min read
Designing Experiments for Two‑Sided Advertising Markets
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Feb 2, 2023 · Operations

Optimizing Xiaohongshu Splash Screen Ads: Flow Selection and Dynamic Decision Mechanisms

Xiaohongshu’s new “traffic‑optimal + dynamic decision” framework models splash‑screen ad allocation as a linear‑programming problem with volume guarantees, continuously adjusts weights via feedback, and pre‑computes cached decisions to preserve fast app startup, thereby boosting click‑through rates while meeting delivery commitments.

CTR improvementLinear ProgrammingSplash Screen
0 likes · 14 min read
Optimizing Xiaohongshu Splash Screen Ads: Flow Selection and Dynamic Decision Mechanisms
DataFunTalk
DataFunTalk
Nov 30, 2022 · Big Data

Design and Practice of Yanxuan A/B Scientific Experiment Platform

The article presents the design, scientific methodology, system architecture, and case studies of Yanxuan's A/B testing platform, detailing how statistical principles, automated tracking, traffic allocation models, and unified reporting accelerate decision‑making and reduce development effort in e‑commerce experiments.

A/B testingautomationdata pipeline
0 likes · 15 min read
Design and Practice of Yanxuan A/B Scientific Experiment Platform
Xingsheng Youxuan Technology Community
Xingsheng Youxuan Technology Community
Jul 13, 2022 · Frontend Development

How Picasso Simplifies Frontend A/B Testing with Efficient Flow and Custom Rules

This article explains how the Picasso platform provides a standardized, high‑efficiency A/B testing pipeline for front‑end developers, covering traffic allocation algorithms, flow reuse, orthogonal and mutually exclusive experiments, multi‑scenario rules, custom metrics, reporting, and future enhancements.

A/B testingCustom Metricsexperiment platform
0 likes · 12 min read
How Picasso Simplifies Frontend A/B Testing with Efficient Flow and Custom Rules
NetEase Yanxuan Technology Product Team
NetEase Yanxuan Technology Product Team
Apr 24, 2022 · Operations

Traffic Distribution and Allocation: Non‑Intervention vs. Intervention, Objectives, and Technical Solutions

The article compares non‑intervention (natural) traffic, where models autonomously maximize UV, with intervention (allocation) traffic that fine‑tunes re‑ranking to meet short‑term business goals, outlines objectives of balancing immediate profit and long‑term value, and presents two technical solutions—an ML‑plus‑OR integer‑programming model and a PID‑based control loop—for real‑time traffic allocation.

OptimizationPID controlReal-time Decision
0 likes · 9 min read
Traffic Distribution and Allocation: Non‑Intervention vs. Intervention, Objectives, and Technical Solutions
DeWu Technology
DeWu Technology
Feb 26, 2021 · Backend Development

Design and Implementation of an AB Testing Platform with Traffic Allocation Algorithms

The paper presents an AB‑testing platform that structures experiments into scenes, buckets, layers and traffic, uses a salted‑hash based allocation and a two‑step “multi‑withdraw‑fill” algorithm to adjust percentages while preserving user‑experiment stability, and describes a lightweight, cache‑centric system architecture with staggered config reloads and safeguards against database spikes and zombie nodes.

AB testingalgorithmexperiment platform
0 likes · 11 min read
Design and Implementation of an AB Testing Platform with Traffic Allocation Algorithms
DataFunTalk
DataFunTalk
Jul 29, 2020 · Artificial Intelligence

Multi‑Business Recommendation System Architecture and Optimization at 58.com

This article explains how 58.com designs a three‑layer recommendation system for its homepage, tackles challenges of multi‑business fusion, interest modeling, traffic allocation, and dynamic refresh, and presents a step‑by‑step optimization pipeline that improves CTR and diversity.

AIdynamic refreshinterest modeling
0 likes · 17 min read
Multi‑Business Recommendation System Architecture and Optimization at 58.com
Beike Product & Technology
Beike Product & Technology
Jan 10, 2019 · Backend Development

Design and Implementation of the AB Experiment Platform at Beike Zhaofang

The article details the design principles, layered traffic allocation model, architecture, data processing pipeline, and operational experience of the AB experiment platform used at Beike Zhaofang, highlighting its web, API, and storage layers, gray‑release capabilities, current limitations, and future improvements.

AB testingbackend architecturedata pipeline
0 likes · 15 min read
Design and Implementation of the AB Experiment Platform at Beike Zhaofang
Hujiang Technology
Hujiang Technology
Jun 27, 2018 · Operations

Design and Architecture of an Overlapping Experiment Platform for Data‑Driven Product Operations

The article describes the motivation, layered design, traffic allocation, statistical validation methods, and system architecture of a scalable A/B testing platform that enables multiple concurrent experiments while ensuring independent traffic segmentation and reliable data analysis for product growth.

A/B testingconfidence intervalexperiment platform
0 likes · 12 min read
Design and Architecture of an Overlapping Experiment Platform for Data‑Driven Product Operations