cold-start — 12 Technical Articles

Dec 8, 2023 · Artificial Intelligence

Multimodal Cold‑Start Techniques for Music Recommendation at NetEase Cloud Music

This article presents NetEase Cloud Music's multimodal cold‑start solution, detailing the problem background, feature selection using CLIP, two modeling approaches (I2I2U indirect and U2I DSSM direct), contrastive learning enhancements, interest‑boundary modeling, and evaluation results showing significant gains in user engagement.

AIcold-startcontrastive learning

0 likes · 15 min read

Multimodal Cold‑Start Techniques for Music Recommendation at NetEase Cloud Music

DataFunSummit

Nov 24, 2023 · Artificial Intelligence

Cold-Start Content Recommendation Practices at Kuaishou

This article describes Kuaishou's approach to cold-start content recommendation, outlining the problems addressed, challenges in modeling sparse new videos, and solutions including graph neural networks, I2U retrieval, TDM hierarchical retrieval, bias correction, and future research directions.

Bias CorrectionKuaishouRecommendation

0 likes · 19 min read

Cold-Start Content Recommendation Practices at Kuaishou

DataFunSummit

Sep 29, 2023 · Artificial Intelligence

Social4Rec: Enhancing Video Recommendation with Social Interest Networks

This article introduces Social4Rec, a video recommendation algorithm that tackles user cold‑start problems by extracting and integrating social interest information through coarse‑ and fine‑grained interest extractors, attention‑based fusion, and extensive offline and online experiments demonstrating significant CTR improvements.

AttentionRecommendationcold-start

0 likes · 14 min read

Social4Rec: Enhancing Video Recommendation with Social Interest Networks

DataFunTalk

Apr 1, 2023 · Artificial Intelligence

Real-Time User Understanding Service (RTUS) for Travel: Architecture, Algorithms, and Experimental Evaluation

This article presents the design and implementation of the Real‑Time User Understanding Service (RTUS) for the Fliggy travel platform, detailing its architecture, multi‑chain data fusion, model and data reuse techniques, and several AI‑driven algorithms for cold‑start interest representation, intent prediction, and destination forecasting, together with extensive offline and online experimental results.

AIIntent PredictionTravel Industry

0 likes · 21 min read

Real-Time User Understanding Service (RTUS) for Travel: Architecture, Algorithms, and Experimental Evaluation

Alimama Tech

Jul 20, 2022 · Artificial Intelligence

Cold-Transformer: Embedding Adaptation for User Cold‑Start Recommendation

Cold‑Transformer tackles the user cold‑start problem by introducing an Embedding Adaption layer that refines sparse user embeddings using context‑aware fused behavior sequences and a label‑encoding scheme, preserving a dual‑tower design and achieving state‑of‑the‑art performance on public and industrial datasets.

Recommendationcold-startembedding adaptation

0 likes · 21 min read

Cold-Transformer: Embedding Adaptation for User Cold‑Start Recommendation

DaTaobao Tech

May 17, 2022 · Artificial Intelligence

Self-Supervised Learning for Image Embeddings in Recommendation Systems: SwAV and M6 Applications at Meiping Meiwu

The paper demonstrates how self‑supervised models SwAV and M6 generate high‑quality image and multimodal embeddings for Meiping Meiwu’s recommendation system, delivering notable gains in scene/style consistency, ranking AUC, classification and retrieval performance, especially for cold‑start items, and achieving measurable production lifts.

A/B testingM6 multimodalSwAV

0 likes · 15 min read

Self-Supervised Learning for Image Embeddings in Recommendation Systems: SwAV and M6 Applications at Meiping Meiwu

DaTaobao Tech

Mar 1, 2022 · Artificial Intelligence

Cold‑Start Optimization for Content Recommendation on Alibaba’s Home‑Decor Platform

Alibaba’s home‑decor platform introduced a two‑stage cold‑start pipeline—Uniform Guarantee and Boost Amplification—combined with a Wide & Deep content‑potential model that predicts new item popularity, dramatically reducing exposure latency, boosting click‑through rates by ~8 % and overall exposure by 13 %.

A/B-testingAlibabaContent Distribution

0 likes · 13 min read

Cold‑Start Optimization for Content Recommendation on Alibaba’s Home‑Decor Platform

DataFunTalk

Jan 23, 2022 · Artificial Intelligence

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation

This article presents a systematic study of recall techniques for new‑user cold‑start in content recommendation, describing a baseline two‑tower model, a Dual Attention Network (DAN) fusion approach, and an enhanced Contextual‑Gate DAN that dynamically balances content and product sequences, together with offline and online evaluation results and future directions.

RecommendationUser Embeddingcold-start

0 likes · 12 min read

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation

DataFunSummit

Aug 21, 2021 · Artificial Intelligence

Cold‑Start Recommendation: Algorithmic Approaches and Strategies

This article reviews algorithmic solutions for cold‑start recommendation, covering the efficient use of side information, knowledge graphs, cross‑domain transfer, multi‑behavior signals, limited interaction data, explore‑exploit tactics, and additional practical scenarios, while summarizing key methods such as DropoutNet, MetaEmbedding, MWUF, MeLU and MetaHIN.

Recommender Systemscold-startcross-domain

0 likes · 11 min read

Cold‑Start Recommendation: Algorithmic Approaches and Strategies

Xueersi Online School Tech Team

Jan 8, 2021 · Artificial Intelligence

Design and Implementation of a Rule‑Based and Collaborative‑Filtering Recommendation System for an Educational App

This article describes the business background, cold‑start challenges, rule‑based recall pipeline, Wilson interval and time‑decay scoring methods, item‑based collaborative filtering implementation with code, and experimental results that improved click‑through rates for the 学而思网校 educational application.

A/B-testRecommendationWilson-interval

0 likes · 12 min read

Design and Implementation of a Rule‑Based and Collaborative‑Filtering Recommendation System for an Educational App

DataFunTalk

Nov 11, 2020 · Artificial Intelligence

Cold-Start Optimization for Feed Ads: Algorithm Design and Experimental Evaluation

In this live talk, Dr. Zhang Renyu, an assistant professor at NYU Shanghai and economist at Kuaishou, presents his research on optimizing cold-start problems in feed advertising using a novel Shadow Bidding with Learning (SBL) algorithm, detailing its design, implementation, and experimental results.

advertisingalgorithmcold-start

0 likes · 4 min read

Cold-Start Optimization for Feed Ads: Algorithm Design and Experimental Evaluation

DataFunTalk

Sep 23, 2019 · Artificial Intelligence

Understanding UC International Feed Recommendation: Goal Determination, Multi‑Objective Estimation, and Mixed Ranking

This article explains how UC international feed recommendation tackles goal definition, multi‑objective point estimation using models such as ESMM, DBMTL and MMoE, mixed‑ranking optimization, and cold‑start challenges by leveraging content understanding and feature generalization to improve user satisfaction.

AIRecommendationcold-start

0 likes · 12 min read