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The article introduces STAN, a multi‑task recommendation framework that leverages user lifecycle segmentation to jointly optimize CTR, stay‑time, and CVR, detailing the business context, key challenges, solution architecture, offline and online evaluations, and future research directions.
This article reviews Shopee and Tsinghua University's latest work on multi‑task CVR optimization, introducing the Click‑aware Structure Transfer with Sample Weight Assignment (CSTWA) method, which tackles knowledge sharing and conflict between CTR and CVR through a three‑part architecture, and demonstrates its superior performance on industrial and public datasets.
This article introduces STAN, a user‑lifecycle‑aware multi‑task recommendation model proposed by Shopee that refines CTR, CVR, and stay‑time predictions by identifying and tracking user states, demonstrates offline gains on Shopee and public datasets, and reports online improvements in click‑through, dwell‑time, and order metrics.
This article outlines the challenges of multi-task and multi-scenario modeling for large-scale C-end services, reviews key industry approaches such as Shared-Bottom, MMoE, PLE, ESMM, and LHUC, and details Zhaozhuan’s own EPNET-based solution that improved click-through and conversion rates.
This article introduces a neuron‑level shared multi‑task learning framework that jointly estimates click‑through rate (CTR) and conversion rate (CVR), discusses the background and advantages of multi‑task learning, reviews classic shared‑bottom models, describes the proposed pruning‑based architecture, and presents experimental results demonstrating its effectiveness in large‑scale recommendation systems.
This article describes the architecture, data processing, city‑intent modeling, resource recall strategies, and multi‑task ranking models—including PLE‑CGC and ESMM—used to improve click‑through and conversion rates of the Tongcheng travel app's homepage waterfall‑flow recommendation, and outlines experimental results and future optimization directions.
This article reviews the evolution of CVR multi‑task learning, introduces a representation‑calibration architecture that shares embeddings between CTR and CVR, details four calibration network designs, reports offline and online AUC improvements, and outlines future research on embedding clustering and loss‑level triplet modeling.
This article reviews the evolution of CVR modeling from early ESMM to recent Calibration4CVR and NCS4CVR, introduces a representation‑calibration architecture that shares embeddings and applies calibrated MLP layers to improve CTR‑CVR multi‑task learning, and reports experimental AUC gains and future research directions.
This article presents Xiaomi’s e‑commerce recommendation research, addressing four key challenges—sample selection bias, data sparsity, delayed feedback, and knowledge inconsistency—by introducing the Entire Space Delayed Feedback with Cross‑Task Knowledge Distillation (ESDC) model, which combines causal inference, cross‑task distillation, twin networks, and uncertainty weighting to improve CVR prediction and achieve a 15% GMV lift over the baseline.
This article presents an adversarial adaptive framework that aligns source and target domains to address domain shift and severe data imbalance in cold-start cross-domain recommendation, demonstrating significant CTR and CVR performance gains when combined with various state‑of‑the‑art single‑domain models.
The Dewu app’s new multi‑objective ranking model replaces the shallow ESMM baseline with a DeepFM‑based MLP and a Deep Interest Transformer that encodes up to 120 recent user actions, adds a dedicated bias network, and fuses short‑ and long‑term interests, achieving modest CTR and CVR AUC improvements while planning future tab‑specific extensions.
The paper introduces DEFUSE and its multi‑task extension Bi‑DEFUSE, unbiased delayed‑feedback CVR models that correct label bias via rigorous importance‑sampling and a latent fake‑negative variable, achieving superior offline performance and a 2 % CVR lift in online deployment compared with existing industry baselines.
Alibaba Mama’s brand advertising value system evolves from instant CTR to deep CVR and causal uplift modeling, employing focal loss, multi‑task training, GAN‑based uplift, enriched user‑sequence and UID embeddings, which together improve conversion lift, QINI, and interaction metrics while mitigating exposure bias and delayed feedback.
This article presents a comprehensive overview of 58's local service recommendation system, detailing the characteristics of its recommendation scenarios, the evolution of tag and post recommendation pipelines, the underlying deep‑learning models such as Bi‑LSTM, ATRank, DeepFM and ESMM, and outlines future research directions.
The Alibaba Mama technical team secured five paper acceptances at The Web Conference 2023, presenting advances in unbiased delayed‑feedback conversion modeling, uncertainty‑regularized knowledge‑distilled CVR debiasing, feature‑aware probability calibration, coordinated two‑stage ad auctions, and scalable decoupled graph neural networks for large‑scale e‑commerce retrieval.
This article presents a comprehensive overview of 58 Local Service's recommendation system, detailing the diverse recommendation scenarios, challenges such as information homogeneity and complex user structures, the multi‑stage recall and ranking pipelines, model evolutions from statistical methods to deep learning, and future work to improve data quality and model efficiency.
The 2021 review of Alibaba’s Mama Search Advertising estimation platform details advances in model deepening—such as hash‑based embedding compression, adaptive dynamic parameters and graph neural networks—model interaction via a multi‑stage cascade with ranking distillation and oracle bias, and system efficiency gains from HPC training, mixed‑precision, multi‑hash embeddings, and fp16 quantization that deliver roughly a thirty‑fold speed‑up.
This article surveys recent industrial multi‑task learning approaches for recommendation, covering models such as Alibaba's ESMM and ESM2, DUPN, Meituan's deep ranking, Google’s MMoE, YouTube’s multi‑objective system, Zhihu’s ranking, and summarizing their architectures, loss functions, and practical gains.
This article presents the design and implementation of a multi‑objective optimization framework using Multi‑gate Mixture‑of‑Experts (MMoE) to improve click‑through, conversion, and purchase behaviors in Taobao's "Lying Flat" home‑goods recommendation channel, detailing model variants, feature engineering, loss weighting, and online A/B test results.
The article explains why recommendation‑system optimization goals must align with business objectives, illustrates this with YouTube’s watch‑time target and Alibaba’s multi‑task CVR/CTR model, and stresses the strategic importance of defining clear goals to guide cross‑team collaboration.