Tagged articles

multi-task learning

112 articles · Page 2 of 2

Dec 30, 2019 · Artificial Intelligence

Multi-Task Learning Models for Recommendation Systems: A Survey of Industrial Applications

Recent industrial advances in multi-task learning for recommendation systems, including Alibaba's ESMM and DUPN, Meituan's deep ranking, Google's MMoE, YouTube's multi-objective ranking, and Zhihu's multi-goal model, demonstrate how shared embeddings and specialized loss functions improve CTR, CVR, and user engagement metrics.

AICTRCVR

0 likes · 14 min read

Multi-Task Learning Models for Recommendation Systems: A Survey of Industrial Applications

58 Tech

Nov 29, 2019 · Artificial Intelligence

Ranking Strategy Optimization Practices for Commercial Traffic at 58.com

This article details the end‑to‑end optimization of 58.com’s commercial traffic ranking system, covering data‑flow upgrades, advanced feature engineering, real‑time and multi‑task model improvements, and a multi‑factor ranking mechanism, while sharing practical results and future directions.

RankingReal-time Data Pipelinefeature engineering

0 likes · 17 min read

Ranking Strategy Optimization Practices for Commercial Traffic at 58.com

DataFunTalk

Oct 16, 2019 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking

This article details Meitu's large‑scale personalized recommendation pipeline, describing the business scenario, challenges of massive data, latency and long‑tail distribution, and the application of deep learning techniques such as Item2vec, YouTubeNet, dual‑tower DNN, NFM, NFwFM and multi‑task learning to improve click‑through rate, conversion and user engagement.

Recommendation Systemsdeep learninglarge-scale

0 likes · 20 min read

Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking

DataFunTalk

Sep 27, 2019 · Artificial Intelligence

Applying Deep Learning to Meitu Community Recommendation: Embedding, Recall, and Ranking Models

The talk by Meitu senior algorithm expert Chen Wenqiang details how deep‑learning‑driven embedding, recall, and ranking techniques—including Item2vec, twin‑tower DNNs, and multi‑task NFwFM—are applied to improve click‑through rates, follow conversions, and user engagement in Meitu's content community.

AIRankingRecommendation Systems

0 likes · 3 min read

Applying Deep Learning to Meitu Community Recommendation: Embedding, Recall, and Ranking Models

DataFunTalk

Jul 23, 2019 · Artificial Intelligence

Technical Exploration of Intelligent Dialogue Robots in Didi Ride-Hailing Scenarios

The talk presents Didi AI Labs' research on intelligent dialogue robots for ride‑hailing, covering single‑turn QA, multi‑turn conversation, multi‑task learning architectures, model experiments, active learning pipelines, and the overall system design that integrates intent detection, slot extraction, dialogue management, and response generation.

AIBERTDialogue Systems

0 likes · 10 min read

Technical Exploration of Intelligent Dialogue Robots in Didi Ride-Hailing Scenarios

DataFunTalk

Dec 28, 2018 · Artificial Intelligence

Zhihu Recommendation Page Ranking: Architecture, Feature Engineering, Model Evolution, and Future Directions

This article presents a comprehensive overview of Zhihu's recommendation page ranking system, covering its request flow, historical ranking evolution, feature design, deep learning models, multi‑task CTR optimization, practical engineering insights, current challenges, and future research directions such as reinforcement learning.

CTRRankingmulti-task learning

0 likes · 15 min read

Zhihu Recommendation Page Ranking: Architecture, Feature Engineering, Model Evolution, and Future Directions

Alibaba Cloud Developer

Aug 17, 2018 · Artificial Intelligence

Can Multi‑Task Learning Shorten E‑Commerce Titles Without Losing Sales?

This paper proposes a multi‑task learning approach that compresses overly long e‑commerce product titles into concise short titles using a Pointer Network, while simultaneously generating user search queries with an attention‑based encoder‑decoder, achieving higher readability, informativeness, and conversion rates than traditional methods.

Attention MechanismSequence-to-SequenceText Summarization

0 likes · 11 min read

Can Multi‑Task Learning Shorten E‑Commerce Titles Without Losing Sales?

Alibaba Cloud Developer

Aug 10, 2018 · Artificial Intelligence

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification Accuracy

This article reviews a 2017 ACM MM paper that introduces a multi‑level similarity‑aware CNN (MSP‑CNN) for person re‑identification, detailing its siamese architecture, dual similarity constraints, multi‑task training, experimental results on CUHK03, Market‑1501 and CUHK01, and its advantages for large‑scale deployment.

CNNdeep learningmulti-task learning

0 likes · 16 min read

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification Accuracy

Alibaba Cloud Developer

Aug 7, 2018 · Artificial Intelligence

How Multi‑Task Learning Can Shrink E‑Commerce Product Titles Without Losing Sales

Researchers propose a multi‑task learning approach that compresses overly long e‑commerce product titles into concise short titles by jointly training a Pointer Network for extraction and an encoder‑decoder for query generation, preserving key information and maintaining conversion rates, as validated by offline and online experiments.

e-commercemulti-task learningnatural language processing

0 likes · 11 min read

How Multi‑Task Learning Can Shrink E‑Commerce Product Titles Without Losing Sales

Alibaba Cloud Developer

Jul 31, 2018 · Artificial Intelligence

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification

This paper introduces a novel multi‑level similarity‑aware CNN (MSP‑CNN) for person re‑identification, applying distinct similarity constraints to low‑ and high‑level feature maps, integrating classification and similarity losses in a multitask framework, and demonstrating superior performance on CUHK03, CUHK01 and Market‑1501 benchmarks.

CNNdeep learningmulti-task learning

0 likes · 15 min read

How Multi-Level Similarity‑Aware CNN Boosts Person Re‑Identification

Alibaba Cloud Developer

Jun 14, 2018 · Artificial Intelligence

Self-Attention Boosts Heterogeneous User Behavior Modeling for Recommendations

This paper proposes a novel attention‑based framework that groups and encodes heterogeneous user behavior sequences into separate semantic subspaces, applies self‑attention to capture inter‑behavior influences, and demonstrates faster training and comparable or improved recommendation performance across multiple tasks and datasets.

Self-AttentionUser Modelingheterogeneous behavior

0 likes · 12 min read

Self-Attention Boosts Heterogeneous User Behavior Modeling for Recommendations

Meituan Technology Team

Mar 29, 2018 · Artificial Intelligence

Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan

The paper describes how Meituan tackles information overload on its lifestyle platform by training multi‑task deep neural networks on billions of interaction logs using a distributed PS‑Lite framework, employing sophisticated feature engineering, missing‑value imputation, KL‑regularization and Neural Factorization Machines to boost offline AUC and online CTR in the “Guess You Like” recommendation feed, while introducing training‑time optimizations and outlining future multi‑task and contextual enhancements.

Recommendation Systemsdeep learningfeature engineering

0 likes · 16 min read

Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan