DeepSeek’s R1, once hailed as a breakthrough AI model with explosive growth, now faces a sharp decline in user traffic and market share, prompting analysis of user migration to third‑party platforms, performance bottlenecks, and contrasting strategies with rivals like Anthropic.
This article presents a novel minute‑level future‑reward framework with dual‑delay incentives, activity‑based attribution, multi‑task delayed modeling, and sequential streaming training that dramatically improves user retention prediction accuracy and real‑time performance in large‑scale recommendation platforms.
This article outlines the current bottlenecks of conventional recommendation pipelines and proposes a comprehensive 2026 research agenda covering retention improvement, user growth, content ecosystem, multi‑objective Pareto optimization, long‑term value modeling, whole‑site optimization, interactive recommendation, personalized modeling, decision‑theoretic formulation, and the OneRec multi‑source fusion framework.
This case study explains how 58 Borrow Money identified low user retention among blue‑collar borrowers, uncovered their needs for emotional recognition and tangible rewards, and responded by designing a virtual coin economy and a tiered growth system, leveraging MidJourney and Stable Diffusion to create compelling visuals that accelerated development and drove revenue growth.
This article analyzes the current bottlenecks of conventional recommendation systems and outlines ten forward‑looking research directions for 2026, including retention improvement, user growth, content ecosystem, multi‑objective Pareto optimization, long‑term value estimation, site‑wide optimization, interactive recommendation, personalized modeling, decision‑theoretic framing, and the integration of large language models via the OneRec framework.
This presentation outlines the current bottlenecks of conventional recommendation pipelines and proposes a 2026 roadmap that includes retention improvement, user‑growth strategies, content‑ecosystem metrics, Pareto‑optimal multi‑objective optimization, long‑term value modeling, site‑wide spatial optimization, interactive recommendation, personalized modeling, and the integration of large‑model fusion through the OneRec framework.
This article provides a comprehensive guide to user operations, covering the definition of user operation, common user analysis techniques, attribute and behavior analysis, segmentation methods using business logic and clustering algorithms, and the concept of the Aha‑moment or magic number for optimizing retention and value.
This article explains why businesses are shifting from ad‑based revenue to tiered paid membership programs, outlines four key benefits such as increased order rates and data‑driven personalization, and details design strategies across the membership lifecycle to attract, retain, and renew high‑value users.
This article explains how Tencent leverages A/B testing across its overseas games, detailing the current market situation, experimental capabilities, multi‑cloud platform architecture, and case studies that illustrate how data‑driven experiments improve user retention, engagement, and overall business growth.
This paper presents RLUR, a reinforcement‑learning algorithm that models user‑retention optimization as an infinite‑horizon request‑based Markov Decision Process, addressing uncertainty, bias, and delayed reward challenges to directly improve retention, DAU, and engagement in short‑video recommendation platforms.
The paper introduces ResAct, a reinforcement‑learning framework that improves long‑term user retention in sequential recommendation by constraining the policy space near the online‑serving policy and employing a conditional variational auto‑encoder, residual actor, and state‑action value network, achieving significant gains over existing methods on a large‑scale short‑video dataset.
This article presents a detailed case study of how a video app team used two rounds of A/B testing with different swipe‑up guide designs to diagnose retention issues, refine the user onboarding experience, and ultimately achieve significant improvements in new‑user retention and engagement metrics.
The 58 Growth System project defines user contribution standards, introduces an eight‑level membership hierarchy with visual cues, medals and swipe animations, and leverages achievement‑based rewards to increase stickiness, foster habit formation, and ultimately drive business conversion for the 58 app.
This article reviews ByteDance's practical growth case where the new video recommendation product “M” used a data‑driven incentive system and extensive AB testing to improve first‑week user retention, outlining the design, implementation steps, and methods for identifying core product functions.
This article examines Baidu’s long‑term “Fruit Garden” operation, detailing how authentic gamified design, habit‑forming mechanics, balanced incentives, and strategic tool integration drive sustained user activity, retention, and product growth within the Baidu App ecosystem.
This article explains why designers must master data analysis, defines what “design data analysis” means, and walks through a three‑step framework—data splitting, tracking, and analysis—illustrated with practical e‑commerce and recruitment case studies to boost product metrics and retention.
This article examines how traditional lottery promotions lose appeal and how applying the Octagonal Behavior Analysis framework, dice‑game mechanics, and targeted blue‑collar user scenarios created a fresh, engaging Spring Travel game that dramatically increased participation, conversion, and retention for a recruitment platform.
This article details how 58 Community evolved its recommendation system from single‑objective click‑rate optimization to a multi‑objective framework that boosts value‑content share, improves user retention, and leverages cross‑domain embeddings and online CEM‑based parameter tuning to achieve significant performance gains.
This article details how 58 Community improved content value share, click‑through, and user retention by designing a generalized multi‑objective recommendation algorithm that leverages cross‑domain embeddings, DeepFM‑DIN models, EGES‑inspired pre‑training, and online CEM‑based parameter optimization.
This case study analyzes how layered entry points, rights‑driven and game‑driven user segmentation, loading and experience optimizations, and unified UI components can transform high‑traffic project entrances into sustainable, engaging traffic with measurable retention improvements.
This article explores how product designers can create lasting user relationships by improving first‑time experiences, boosting retention, and personalizing long‑term interactions through examples like auto‑swipe email handling, Spotify activity feeds, Photoshop action suggestions, Apple Watch notifications, and context‑aware iOS app placement.
The article explains how Ganji.com's "Dream Shop" user growth system uses gamified, story‑driven design, modular architecture, visual diversity, motion effects, and a complete consumption loop to increase user engagement, retention, and product value for blue‑collar workers.
This article provides a comprehensive analysis of the Soul social app, examining its non‑hormonal positioning, community atmosphere, relationship‑chain metrics, content‑driven engagement, algorithmic matching, and future growth strategies, highlighting how these factors drive user retention and scale.
The article analyzes Baidu's strategic shift toward acquiring YY Live and strengthening its mobile content ecosystem, highlighting how the lack of a unified account system and declining user dwell time have driven the search giant to pursue live‑streaming and recommendation‑driven products to diversify revenue and improve user retention.
Ganji transformed from a multi‑service portal into a focused recruitment platform by redesigning job matching, integrating messaging with job management, and building a blue‑collar social network, addressing key user‑loss points and extending the lifecycle of job seekers.
This article explores how applying gamification principles—progressive onboarding, clear pathways, achievement milestones, honor systems, and loss aversion—can transform the 58商家通 merchant app, boosting user engagement and retention by turning routine tasks into compelling game‑like experiences.
This article explains how to build an effective membership system by defining its concept, outlining its business value, and detailing essential design elements such as balanced benefits, clear growth paths, user‑centric scenarios, behavior analysis, and atmospheric branding to sustainably increase user loyalty and profit.
The article examines TikTok's dramatic increase in US user retention by dissecting supply‑side content growth, operational localization, marketing exposure, algorithmic matching, and external influences, and then proposes data‑driven and algorithmic interventions to sustain and amplify the platform's growth.
This article explains how modern internet businesses can achieve rapid, cost‑effective expansion by shifting from the classic AARRR acquisition‑focused model to the retention‑centric RARRA framework, detailing the five growth stages, retention analysis, activation tactics, referral incentives, monetization strategies, and a systematic growth‑hacking methodology.
By extracting hour‑level activity logs and training supervised models—including CART, GBDT, and neural networks—on user tags, the team simulated short‑term metrics for new reward campaigns, enabling earlier prediction of next‑day retention and shortening experiment cycles despite delayed T+1 data.
This article explains why onboarding guides are essential for reducing user learning costs, outlines the content, timing, and presentation methods of effective onboarding, and offers practical tips—such as clear copy, positive feedback, fun elements, brand consistency, and skip options—to create a seamless user experience.
The article analyzes Xiaohongshu’s 77‑day removal, showing a sharp drop in total active users and DAU, a partial rebound driven by loyal users, the scramble for alternative download sources, opportunistic third‑party sellers, and emerging competitors, while highlighting the product‑management challenges of such a disruption.
This article explains how lightweight H5 mini‑games can dramatically boost app metrics such as new user acquisition, profit targets, and daily active users by leveraging engaging gameplay, community virality, cultivation mechanics, and real‑world reward loops, illustrated with real cases from Alipay, Tmall, and Xiaomi Finance.
This case study explains how a data‑driven redesign of the landlord center on the 58 rental platform aligned user and business goals, introduced low‑cost experiments, and leveraged scoring and aggregation features to dramatically increase feature exposure, conversion rates, and overall user retention.
Using Python, Scrapy, pandas and seaborn, this article crawls public Shanbay user data, cleans and stores it in PostgreSQL, then analyses retention, growth and word‑learning distributions, revealing that the overwhelming majority of learners abandon the app within a few days.
The article explains why operators should start from clear goals rather than events, defines meaningful metrics such as user retention and API call volume, shows how to break down and evaluate these metrics, and offers practical advice on data collection, benchmarking, and continuous improvement.
This article outlines a comprehensive internet operations framework that covers the four essential stages—acquisition, retention, conversion, and activation—detailing practical strategies for attracting users, keeping them engaged, encouraging purchases, and fostering ongoing activity through various marketing channels and user‑experience improvements.
An in‑depth look at Southeast Asia’s mobile gaming landscape reveals how traffic, content localization, payment methods, operational partnerships, and user‑retention strategies shape success across Thailand, Indonesia, Philippines, Vietnam, Malaysia and Singapore, offering actionable insights for developers targeting this fast‑growing region.
This article explains how game developers and marketers use internal, platform, and external data to forecast product performance, optimize user acquisition, predict market trends, model retention curves, and make informed budgeting decisions throughout a game's lifecycle.
