The article analyzes cutting‑edge agentic RAG designs, LLM‑enhanced recommendation pipelines, and generative ranking models from Alibaba Cloud, Huawei Noah, and Baidu, detailing their architectures, multi‑modal retrieval strategies, GPU acceleration, and measured performance gains.
The article reviews cutting‑edge AI search and recommendation techniques—including Agentic RAG, multi‑modal retrieval, GPU‑accelerated indexing, and Baidu’s generative ranking model GRAB—detailing their architectures, optimization strategies, and measured performance gains such as a 1.5% AUC lift.
The article reviews a collection of technical chapters that analyze how multi‑agent AI architectures, large‑language‑model enhancements, and generative ranking models are applied to solve high‑concurrency, multimodal, and multi‑hop challenges in modern recommendation and search systems, presenting concrete designs, performance numbers, and real‑world case studies.
The Kuaishou technology team had 25 papers accepted at the prestigious KDD 2026 conference—including three oral presentations—covering generative recommendation, automated bidding, semantic ID learning, multi‑behavior modeling, and other AI‑driven advances that are already deployed at massive scale on the platform.
Airbnb built a Transformer‑based sequence model that encodes years of guest behavior—including long‑term bookings and short‑term browsing—to deliver timely, personalized search results, achieving up to 3.78% overall ranking improvement and significant gains in bookings and clicks.
The article reviews a series of technical case studies—including Alibaba Cloud AI Search's Agentic RAG, Baidu's GRAB generative ranking, Huawei Noah's LLM‑enhanced recommendation, and Elasticsearch vector RAG—showing how multi‑agent AI architectures address high‑concurrency, multimodal, and multi‑hop query challenges while delivering measurable performance gains.
The article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation evolution, and Baidu's generative ranking model GRAB—detailing their architectures, multi‑modal retrieval strategies, performance gains, and real‑world deployment insights.
The article analyzes how agentic architectures, large language models, and generative ranking techniques are applied to overcome high‑concurrency, multimodal, and multi‑hop challenges in modern recommendation and search systems, showcasing concrete designs, performance gains, and real‑world deployments from Alibaba Cloud, Huawei Noah, and Baidu.
A comprehensive dialogue between Hong Kong Chinese University’s vice president and Tencent’s advertising AI experts explores AI frontiers, trustworthy and embodied AI, the four‑generation evolution of recommendation systems, the role of the Tencent Advertising Algorithm Competition in talent development, and practical advice for young professionals navigating the AI‑driven advertising industry.
Recent research argues that the traditional platform‑centric recommendation paradigm is reaching its limits, proposing a user‑governed personalization model enabled by LLM agents that can aggregate cross‑platform data, with experimental evidence showing significant performance gains over platform‑only approaches.
The article analyzes the technical evolution of AI‑driven recommendation and search, covering Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB, while presenting design choices, performance metrics, and real‑world deployment results.
The article examines how large‑language models can augment a game‑distribution recommender by keeping accurate ranking while adding an expression and decision layer that explains differences between similar titles, using a structured schema, an exploration‑to‑convergence workflow, and engineering safeguards to make the insights stable and reusable.
The article analyzes cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommender, and Baidu's generative ranking model—detailing their architectures, multi‑modal retrieval strategies, performance gains, and practical deployment insights.
This article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud’s Agentic RAG, Huawei’s LLM‑enhanced recommendation pipeline, and Baidu’s generative ranking model GRAB—detailing their architectural evolution, multimodal retrieval strategies, performance benchmarks, and practical deployment insights.
The article presents UserLLM, a three‑stage framework—generative pre‑training, discriminative SFT, and CTR fusion—that leverages ultra‑long user behavior sequences to address the compression and scaling limits of traditional discriminative ranking models, demonstrating significant offline gains and validated scaling‑law behavior across token, layer, and width dimensions.
Kuaishou’s new Recommendation-as-Generation (RaG) framework replaces traditional retrieve-and-rank with a generative pipeline that predicts user interests, creates personalized video content, and closes the loop with feedback, delivering a 1.87% ad‑revenue lift for over 400 million daily users.
LeAP replaces costly permutation‑based feature importance with an end‑to‑end learnable module that shuffles features, applies adaptive gradient‑bias regularization, and achieves state‑of‑the‑art sparsity and inference speedups on large‑scale recommender models.
This article analyzes cutting‑edge AI search and recommendation technologies, covering Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommender evolution, and Baidu's generative ranking model GRAB, each with detailed designs, performance metrics, and real‑world deployment insights.
This article introduces LWGR, a framework that uses personalized soft prompts generated by parallel codebooks and Lagrangian‑constrained knowledge fusion to integrate large language model world knowledge into generative recommendation, overcoming fixed‑prompt limitations and knowledge‑behavior conflicts, and demonstrates superior performance on public and industrial datasets with notable revenue gains in online A/B tests.
This article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommender, and Baidu's generative ranking model GRAB—detailing their architectures, multi‑modal retrieval strategies, performance gains, and practical deployment insights.
OneReason introduces a systematic reasoning capability into industrial recommendation models through multi‑stage pre‑training, chain‑of‑thought fine‑tuning, and reinforcement learning, achieving significant gains in click‑through, revenue, and cross‑domain recommendation performance while preserving the underlying language abilities of the base model.
The article reviews cutting‑edge technical practices for next‑generation recommendation and search, covering Alibaba Cloud AI Search's Agentic RAG multi‑agent design, Huawei Noah's LLM‑enhanced recommendation evolution, Baidu's generative ranking (GRAB) for ads, and Elasticsearch‑based vector RAG implementations, with concrete architecture details and performance results.
The article details a multi‑layer Research Agent system that automates the repetitive execution steps of recommendation model development, demonstrates a 17.5% HitRate@100 lift on Lazada’s generative recall pipeline, and argues that while agents excel at efficient trial‑and‑error, true creative breakthroughs still require human insight.
Alibaba International hosted an Academic Bar at Zhejiang University where experts presented multilingual e‑LLM models, next‑generation recommendation systems, AI‑driven optical design, safety frameworks for agents, a benchmark for e‑commerce chatbots, virtual user research, an Agent Harness architecture, and edge‑side large‑model techniques, illustrating the practical convergence of cutting‑edge AI research and industry.
The article highlights thirteen Taobao‑group papers accepted at KDD 2026, covering large‑model scaling laws, end‑to‑end generative recommendation, CTR prediction, interactive recommendation agents, LLM‑based pricing, robust auto‑bidding, two‑stage auctions, generative world models, multi‑attribution conversion, uplift modeling and long‑term causal estimation for e‑commerce systems.
The article analyzes cutting‑edge AI search and recommendation technologies—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB—detailing their architectural evolution, multi‑modal retrieval strategies, GPU acceleration gains, and measured performance improvements.
SORT re‑architects industrial‑scale ranking by shifting to a request‑centric data paradigm, integrating sparse and MoE optimizations into a Transformer backbone, and delivering significant CTR‑AUC, FLOPs, and online metric improvements while maintaining high training and inference efficiency.
The article reviews a collection of technical chapters that analyze how multi‑agent AI architectures, large‑language‑model‑enhanced recommendation pipelines, generative ranking for ads, and Elasticsearch‑based vector RAG are applied to build next‑generation recommendation and search systems, citing concrete designs, performance numbers and real‑world deployments.
The article reviews cutting‑edge AI techniques for high‑concurrency, multimodal recommendation and search, detailing Alibaba Cloud's Agentic RAG evolution, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB, each with architecture diagrams, performance metrics, and real‑world deployment insights.
The article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommender, Baidu's generative ranking model GRAB, and Elasticsearch‑based vector RAG—detailing their challenges, architectural evolutions, performance gains, and real‑world deployment results.
This article reviews cutting‑edge AI search and recommendation technologies, covering Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB, while detailing their design challenges, multi‑modal retrieval strategies, performance gains, and real‑world deployment results.
The article reviews the evolution of recommendation systems from deep learning to large language models, analyzes core challenges such as noisy implicit feedback and limited semantic understanding, and details Huawei Noah’s KAR solution that uses factorized prompting, multi‑expert adapters, and AI‑Agent architectures to achieve a 1.5% AUC lift and validated online A/B test results.
This article reviews cutting‑edge AI search and recommendation techniques—including Alibaba Cloud's Agentic RAG, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB—detailing their architectural evolution, multimodal retrieval strategies, GPU acceleration, and measured performance gains.
This article summarizes a technical ebook that analyzes the evolution of recommendation and search systems—from deep‑learning models to large‑language‑model agents—detailing multi‑agent RAG architectures, Huawei’s KAR knowledge adapters, Baidu’s generative ranking (GRAB), Elasticsearch vector search, and performance results such as a 1.5% AUC lift and GPU‑accelerated throughput gains.
The article explains how big‑data techniques such as collaborative filtering, time‑aware recommendations, and behavior‑sequence profiling transform e‑commerce and retail by revealing individual consumer intent, boosting conversion rates by up to 30% and enabling more precise pricing and inventory decisions.
This article reviews eight Kuaishou‑authored papers accepted at ICLR 2026, summarizing their problem statements, novel methods such as front‑door causal attribution, visual table retrieval, denoising rerankers, difficulty‑adaptive reasoning, diffusion code infilling, generative ordinal regression, multimodal video retrieval, e‑commerce dialogue benchmarks, and a new LLM creativity evaluator, together with reported experimental gains.
Alibaba International Intelligent Technology showcases nine industrial‑grade recommendation and search papers accepted at SIGIR, WWW, and WSDM 2026, detailing sparse scaling, counterfactual multi‑task learning, generative recommendation, MoE routing, and multimodal semantic ID breakthroughs with extensive offline and online results.
This article reviews cutting‑edge technical practices from Alibaba Cloud AI Search, Huawei Noah's recommendation platform, and Baidu's GRAB model, detailing how multi‑agent RAG architectures, large‑language‑model enhancements, and generative ranking overcome high‑concurrency, multi‑modal data, and feature‑engineering bottlenecks.
The article analyzes the evolution from traditional modular recommendation models to a generative Large User Model (LUM), detailing its three‑stage paradigm, tokenization, training objectives, scaling‑law findings, offline and online experiments, and the AI‑infra innovations that enabled a 4.5% CTR lift in production.
The 2026 KDD Cup, powered by Tencent’s Advertising Algorithm Competition with an $885,000 prize pool, challenges participants to unify sequence modeling and feature interaction in large‑scale recommendation systems, offering academic publication paths, real‑world deployment opportunities, and strict latency constraints that push both research and engineering innovation.
On March 20 2026, Alibaba’s Qwen 3.5‑Max topped the LMArena blind‑test, OpenAI bought Astral to boost AI coding, Zhejiang University released a real‑time 4D world model, Meta’s Agent leaked data, and a series of AI‑driven innovations from Nvidia, robotics to drug discovery reshaped the industry.
This survey examines the emerging LLM‑RL collaborative recommendation paradigm, outlining its research background, five main collaboration patterns, standardized evaluation protocols, and the key challenges and future directions for building smarter, more robust recommender systems.
Meta is secretly testing an AI shopping assistant that leverages billions of users' social profiles, shifting recommendation logic from reactive behavior data to proactive identity‑driven suggestions, while raising significant privacy and ecosystem implications for e‑commerce.
This article explains how advertising recommendation is evolving from traditional feature‑engineered models to LLM‑driven pipelines, detailing data‑infrastructure challenges, semantic upgrades with multimodal embeddings, case studies in short‑video ads, user cold‑start prompt engineering, and future directions for generative recommendation systems.
The paper introduces CSMF, a cascaded selective‑mask fine‑tuning framework for dual‑tower embedding‑based retrieval that sequentially optimizes exposure, click and conversion goals, incorporates cumulative percentile pruning and adaptive margin loss, and achieves measurable gains in ad revenue and CTR without increasing model size or latency.
This article examines the evolution of re‑ranking systems from traditional pointwise models to a two‑stage generation‑evaluation framework, compares autoregressive and non‑autoregressive generative approaches, details inference speed optimizations with GPU and model‑server upgrades, and outlines a future end‑to‑end sequence generation architecture enhanced by reinforcement learning and contrastive learning.
This article details Ximalaya's journey from traditional multi‑stage recommendation pipelines to generative AI‑driven models, covering business challenges, architectural and model differences, phased deployments, knowledge distillation, semantic ID encoding, decoder‑only strategies, extensive offline and online evaluations, and future research directions.
This paper introduces the TCA4Rec framework that directly aligns item‑level collaborative‑filtering preferences with token‑level objectives of large language models, presenting novel modules, extensive experiments, and analysis that demonstrate significant performance gains in generative recommendation tasks.
RecFlow, a new inference engine from Beijing University of Posts and Telecommunications and Meituan, tackles the resource mismatch of DLRM models by coordinating embedding and DNN operators at the intra‑SM level and introducing interference‑aware adaptive scheduling and incremental batching, achieving up to 9.34× higher throughput on RTX 3090.
OneSug introduces an end‑to‑end generative framework that unifies recall, coarse‑ranking, and fine‑ranking for e‑commerce query suggestion, addressing the limitations of traditional multi‑stage cascades and dramatically improving relevance, efficiency, and business metrics in real‑world deployments.
The DMF framework introduces a modality‑enriched Decoupled Target Attention (DTA) and a Complementary Modality Modeling (CMM) strategy that together close the semantic gap between ID and multimodal features, delivering up to 5.3% CTCVR lift, 7.43% GMV increase, and a three‑fold throughput gain in large‑scale e‑commerce recommendation scenarios.
This article summarizes three cutting‑edge AI techniques—Alibaba Cloud's Agentic RAG architecture for multimodal search, Huawei Noah's large‑model‑driven recommendation system evolution, and Baidu's generative ranking (GRAB) model for ads—detailing their challenges, designs, performance gains, and practical deployment insights.
The article analyzes the severe inference bottlenecks of large language models, CNNs, and recommendation systems and presents a suite of research‑driven accelerations—including token‑level pipeline parallelism (HPipe), KV‑cache clustering (ClusterAttn), quantization (QoKV), heterogeneous edge frameworks (DeepZoning, PICO), delay‑aware edge‑cloud scheduling (DECC), and operator choreography (RACE)—validated on real‑world industrial workloads.
This article surveys cutting‑edge AI techniques—including Alibaba Cloud's Agentic RAG for multimodal search, Huawei Noah's LLM‑enhanced recommendation evolution, and Baidu's generative ranking (GRAB) for ads—detailing their architectures, optimization tricks, performance gains, and real‑world deployment results.
This article explains a SIGIR 2025 paper that tackles cold‑start click‑through‑rate prediction in JD's ad system by using a Graph Isomorphism Network‑based cohort modeling framework, detailing its three‑module architecture, extensive experiments on public and industrial datasets, and a live deployment that achieved a 2.13% CTR lift.
This article reviews three cutting‑edge AI search and recommendation techniques—Alibaba Cloud's Agentic RAG architecture, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's GRAB generative ranking model—detailing their design challenges, multi‑modal retrieval strategies, performance gains, and real‑world deployment results.
The article reports that CIKM 2025 in Seoul featured three Alibaba International Intelligent Technology papers on multimodal recommendation and dynamic reserve pricing, and that the team organized the AnalyticCup competition and a workshop to advance multilingual e‑commerce search using large language models.
The Datafun Summit 2025 talk by JD’s algorithm engineer Tian Mingyang explains how generative AI is driving a paradigm shift in recommendation systems, detailing the limits of traditional models, the new dynamic cross‑domain inference chain technique, joint engineering‑algorithm optimizations, and the remaining challenges for future deployment.
This article summarizes cutting‑edge techniques from Alibaba Cloud AI Search’s Agentic RAG architecture, Huawei Noah’s LLM‑enhanced recommendation evolution, and Baidu’s GRAB generative ranking model, detailing multi‑agent retrieval, multimodal data handling, scaling laws, causal attention, and performance gains demonstrated through benchmarks and real‑world deployments.
Alibaba’s 1688 platform leverages inference‑based large language models to enhance recommendation discovery, addressing the filter‑bubble problem by analyzing long‑term buyer behavior, compressing extensive activity streams, generating nuanced demand queries, and integrating multimodal data and market trend agents to deliver more diverse, explainable product suggestions for B‑type buyers.
The paper introduces MMQ, a multimodal mixture‑of‑quantization semantic‑ID framework that compresses item multimodal features via shared‑specific expert networks and behavior‑aware fine‑tuning, achieving lower reconstruction loss, superior recall and ranking performance, and online gains of +1.29% REV, +4.33% CVR, +2.61% GMV, and +1.18% ROI.
This article reviews Kuaishou's two‑year exploration of large‑model techniques in advertising, detailing the challenges of content‑domain ad estimation, the use of multimodal and LLM technologies to harness full‑scope user behavior and external knowledge, and the COPE and LEARN frameworks that delivered measurable business gains.
This article surveys the evolution of generative recommendation systems powered by large language models, detailing their technical foundations, engineering challenges, recent breakthroughs, and future research directions, while highlighting why the paradigm shift is occurring now.
This article explores how multimodal large models like Qwen2.5‑VL enable high‑dimensional tag generation and universal embeddings for recommendation systems, detailing data synthesis, model training, quantization, fine‑tuning, and the resulting improvements in click‑through rate and exposure interaction.
EMER, Kuaishou’s end‑to‑end multi‑objective ensemble ranking framework, replaces handcrafted scoring formulas with a transformer‑based model that learns comparative preferences, integrates normalized rank features, optimizes relative satisfaction and multi‑dimensional proxy metrics, and dynamically balances objectives via a self‑evolving advantage evaluator, delivering significant online gains.
This article details the EMER framework—a Transformer‑based, end‑to‑end multi‑objective ranking system that replaces handcrafted formulas with a learnable AI model, introduces relative‑satisfaction signals and dynamic loss weighting, and demonstrates significant offline and online performance gains in Kuaishou's short‑video recommendation pipeline.
This paper introduces IAK, a unified multi‑domain recommendation paradigm that treats the system as a large model, leveraging pre‑training and fine‑tuning with an information‑aware adaptive kernel to capture rapid user interest shifts while reducing training costs and improving online performance.
This article reviews Kuaishou's two‑year exploration of multimodal large‑model techniques for advertising, outlining challenges in content‑domain ad estimation, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together improve ad system performance.
This article details Baidu's generative recommendation ad recall framework, introducing the COBRA system and its three development stages—dense representation compression, sparse quantization with ID generation, and dense‑sparse cascading—highlighting coarse‑to‑fine inference, performance gains, long‑sequence extensions, online deployment, and future research directions.
This article reviews Kuaishou's two‑year exploration of large‑model techniques in advertising, outlining challenges in content‑domain ad estimation, introducing the COPE unified content representation framework and the LEARN LLM knowledge‑transfer approach, and showing how these innovations delivered tangible business gains.
This article reviews Kuaishou's two‑year exploration of multimodal large‑model techniques for advertising, detailing challenges of fragmented user behavior, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together delivered measurable business gains.
RecIS is an open‑source, PyTorch‑based unified framework designed for ultra‑large‑scale sparse‑dense computation in recommendation systems, offering a full solution for training models with massive samples, multimodal inputs, and large embeddings, and demonstrating significant performance gains over TensorFlow and TorchRec in production deployments.
This article outlines Kuaishou's two‑year exploration of large‑model techniques in advertising, detailing challenges of sparse cross‑domain data, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together improve ad system effectiveness.
Over the past two years, Kuaishou has leveraged multimodal large‑model techniques to overcome sparse advertising data, integrating full‑domain user behavior and external knowledge via the COPE unified product representation framework and the LEARN LLM knowledge‑transfer system, achieving measurable business gains.
The article presents the OnePiece framework, which injects LLM‑style context engineering and latent reasoning into item‑ID based search‑and‑recommendation models, details the design choices, training tricks, attention analysis, and reports online gains of around 1% GMV and ad revenue, offering a thorough technical dissection of generative recommendation in industrial settings.
This article reviews Huawei Noah's Ark Lab's exploration of large language models in recommendation systems, covering background challenges, the KAR and Uni-CTR projects, experimental results, and future research directions for open, knowledge‑driven recommendation pipelines.
This presentation explores AI-era recommendation strategies, defines strategy product roles and ability models, outlines the three generations of product managers, discusses AI-driven trends, workflow simplifications, 2024 observations, and offers practical guidance for career growth in product management.
This article explains how JD Health’s recommendation team integrates large‑model technologies—scaling CTR models, enhancing pipelines with LLMs, and adopting generative models—into e‑commerce recommendation systems, highlighting practical applications and technical challenges specific to the health‑commerce sector.
This article explains Baidu's generative ranking model GRAB, detailing how scaling laws from large language models inspire a new recommendation paradigm, the model's architecture, custom attention mechanisms, training strategies, deployment optimizations, and the resulting business gains in CTR and revenue.
The author recounts how, by modeling user intent with a multi‑layer Gaussian‑based PODM‑MI framework and addressing a novel ‘sand‑glass’ bottleneck in RQ‑VAE semantic identifiers, JD’s search ranking achieved significant UCVR gains, annual order increases of over ten million, and a SIGIR 2024 paper acceptance.
The article analyzes the FilterLLM approach, which augments a frozen LLM with billions of learnable user tokens to predict a full‑user interaction probability distribution in a single forward pass, dramatically speeding up cold‑start recommendation while preserving recommendation quality across multiple benchmarks.
Eight Kuashou research papers covering recommendation systems, multi‑task learning, multimodal large models, large language models, and combinatorial optimization have been accepted to the premier AI data‑mining conference KDD 2025, highlighting the company’s cutting‑edge innovations and their potential impact on the field.
This article introduces LCRON, a novel end-to-end training framework for cascade ranking systems that aligns training objectives with overall recall, addresses stage interaction challenges, and demonstrates significant performance gains on public benchmarks and in Kuaishou’s commercial advertising platform.
This article presents LAST, a novel Learning-at-Serving-Time approach that enables real‑time online learning for re‑ranking in industrial recommendation pipelines, eliminating feedback latency, detailing its architecture, challenges, experimental validation, and practical advantages over traditional online learning methods.
In an interview with Vivo AI engineer Liang Tianan, the article explores the challenges of post‑Q&A recommendation, the integration of large language models into recall, ranking and evaluation pipelines, and the engineering trade‑offs required to deliver high‑quality, diverse suggestions on mobile devices.
Join Baidu senior algorithm engineer Ji Zhi at the DataFun Summit 2025 to explore how generative AI transforms ad recommendation recall, covering item representation, evolving solution architectures, long‑sequence challenges, and practical insights for building efficient large‑model recommendation systems.
This article explains EasyRec's end‑to‑end recommendation system, covering its training‑inference architecture, a series of CPU/GPU and distributed optimizations, and a real‑time online‑learning pipeline that together improve throughput, latency, and model freshness.
Meituan’s recommendation team introduced the MTGR framework, aligning traditional DLRM features with a unified HSTU‑based Transformer to explore scaling laws, delivering a 65‑fold FLOPs boost, 12% lower inference cost, and record gains in online CTR and order volume across its food‑delivery platform.
This announcement introduces a live session that will dissect two best‑paper award research works from WSDM 2025—one revealing how recommendation models amplify popularity bias through spectral analysis and proposing a lightweight regularizer, and the other presenting a graph disentangle causal model that integrates GNNs with structural causal models to improve causal inference on networked observational data.
The award‑winning WSDM 2025 paper reveals how recommendation models magnify popularity bias through spectral alignment of the rating matrix’s leading singular vector with item popularity, and proposes the ReSN regularization technique to mitigate this bias efficiently.
This article reviews recent advances in applying large language models to CPS advertising recommendation, outlines business requirements and core technical challenges, proposes an extensible multi‑task generative framework with explicit intent perception and multi‑objective optimization, and presents offline and online performance gains along with future research directions.
This paper investigates the "sandglass" phenomenon in residual‑quantized semantic identifiers for generative search and recommendation, analyzes its causes of path sparsity and long‑tail token distribution, and proposes heuristic and adaptive token‑removal methods that substantially improve model performance in e‑commerce scenarios.
UQABench introduces the first benchmark for assessing high‑density user embeddings that serve as soft prompts in LLM‑driven recommendation, featuring a three‑stage pre‑train‑align‑evaluate pipeline, seven personalized QA tasks, and findings that transformer encoders, side‑information, simple linear adapters, and larger models markedly improve accuracy while cutting input tokens to about five percent.
Alibaba’s Taobao Group unveiled its AIGX technology suite, including the RecGPT recommendation model, the AIGB generative bidding system, and a new AI‑generated video engine, detailing open‑source benchmarks, NeurIPS workshop participation, and measurable ROI improvements for e‑commerce advertising.
This article presents a comprehensive case study on how large language models are integrated into 58.com’s real‑estate recommendation platform, detailing challenges, data adaptation, prompt and parameter optimizations, embedding generation, conversational recommendation, and future directions for multimodal and generative recommendation systems.
This article provides a step‑by‑step tutorial that walks readers through the fundamentals of traditional machine‑learning algorithms, feature‑engineering techniques, model training pipelines, evaluation metrics, and then advances to deep‑learning concepts such as MLPs, activation functions, transformers, and modern recommendation‑system models.
This comprehensive guide walks readers through the evolution of machine learning, starting with basic linear models and feature engineering, progressing through logistic regression, decision trees, and deep learning architectures like MLPs, CNNs, RNNs, and transformers, and demonstrates practical implementations with code examples and evaluation metrics.
This article reviews a decade of recommendation‑system research, outlines the shift from traditional listwise methods to deep‑learning models, discusses the impact of large language models and AI agents, and presents future directions such as multimodal interaction, responsible AI, cognitive modeling, and ecosystem integration.
The article presents two context‑enhanced methods—ECUP for full‑chain uplift modeling and NLGR for neighbor‑list reranking—demonstrating significant gains in coupon issuance and ranking on Meituan’s aggregation page through extensive offline and online experiments.
The article details Qunar’s hotel search ranking system evolution, covering the shift from rule‑based sorting to LambdaMart, the adoption of LambdaDNN deep models, multi‑objective MMOE architectures, multi‑scenario integration, extensive feature engineering, and experimental results demonstrating significant offline and online performance gains.
