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Ximalaya Technology Team

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Latest from Ximalaya Technology Team

45 recent articles

Ximalaya Technology Team

May 27, 2026 · Artificial Intelligence

Ximalaya’s LLM‑Powered Interactive Recommendation System: Architecture and Results

The article details Ximalaya’s three‑layer interactive recommendation architecture—PBox for parameter control, an LLM‑driven Agent for intent understanding, and the iSUG interface—showing how natural‑language‑based parameter tuning shifts the paradigm from one‑way push to two‑way dialogue and significantly improves recommendation efficiency and user retention.

FunctionCallingInteractiveLLM

0 likes · 17 min read

Ximalaya’s LLM‑Powered Interactive Recommendation System: Architecture and Results

Ximalaya Technology Team

Feb 11, 2026 · Artificial Intelligence

How Ximalaya Used Generative AI to Revolutionize Audio Recommendations

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.

Encoder-DecoderGenerative AIaudio recommendation

0 likes · 24 min read

How Ximalaya Used Generative AI to Revolutionize Audio Recommendations

Ximalaya Technology Team

Nov 29, 2024 · Artificial Intelligence

Applying Large Language Models for AIGC Advertising: Content Generation, Multimodal Understanding, and Creative Optimization at Ximalaya

Ximalaya leverages large language models and AI‑generated content to automate ad creative production, multimodal semantic understanding, and creative selection, slashing image costs to 0.2 CNY, boosting CTR by up to 3.5 %, improving revenue and eCPM by over 2 %, and expanding material diversity fivefold.

AIGCcreative optimizationlarge language models

0 likes · 21 min read

Applying Large Language Models for AIGC Advertising: Content Generation, Multimodal Understanding, and Creative Optimization at Ximalaya

Ximalaya Technology Team

Jul 12, 2024 · Artificial Intelligence

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

In real‑time bidding advertising, a multi‑path recall framework quickly filters billions of ads using parallel non‑personalized and personalized strategies—such as hot‑item rules, collaborative‑filtering, skip‑gram vectors, and GraphSAGE embeddings—while respecting targeting constraints, before a ranking stage optimizes eCPM, with effectiveness measured offline and online and future extensions planned with large language models.

AdvertisingGraph Neural Networkmachine learning

0 likes · 18 min read

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

Ximalaya Technology Team

Apr 30, 2024 · Artificial Intelligence

Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine

The advertising engine uses a five‑stage funnel—retrieval, recall, coarse ranking, fine ranking, and re‑ranking—each optimized with specialized indexes, multi‑channel recall, multi‑objective twin‑tower models, deep CTR/CVR predictors, and cold‑start paths, delivering up to 33 % spend growth, 6 % eCPM lift and lower latency while maintaining diversity.

Advertisingcold starteCPM

0 likes · 15 min read

Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine

Ximalaya Technology Team

Feb 20, 2024 · Artificial Intelligence

Optimization of Deep Learning-Based CTR Models in Advertising

This report presents recent advances in optimizing deep learning click‑through‑rate models for advertising, including improved embedding mechanisms, novel feature‑interaction and architecture designs such as attention‑based behavior sequencing, multi‑tower and Mixture‑of‑Experts networks, dynamic ID handling, hourly updates, incremental training, and outlines future multi‑modal and embedding‑importance research.

CTR modelEmbedding Techniquesadvertising technology

0 likes · 13 min read

Optimization of Deep Learning-Based CTR Models in Advertising

Ximalaya Technology Team

Feb 1, 2024 · Artificial Intelligence

Understanding AI Image Generation: Diffusion Models, CLIP, and Control Techniques

This guide explains how AI image generators such as Stable Diffusion and DALL·E 3 turn text prompts into pictures by using diffusion models, CLIP‑aligned embeddings, and optional controls like negative prompts, fine‑tuned LoRA checkpoints and ControlNet conditioning, highlighting their differences, workflow, and practical customization.

AI image generationCLIPControlNet

0 likes · 18 min read

Understanding AI Image Generation: Diffusion Models, CLIP, and Control Techniques

Ximalaya Technology Team

Jan 9, 2024 · Big Data

Deep Advertising Conversion Optimization at Ximalaya

Ximalaya’s deep advertising conversion optimization advances from shallow to deep billing models by integrating OCPC dual‑bidding, full‑channel data assistance, and real‑time crowd premium to overcome data sparsity, long conversion delays, and cold‑start challenges, boosting advertisers’ ROI while managing platform risk and guiding future ROI‑protected bidding.

AdvertisingOCPCROI

0 likes · 27 min read

Deep Advertising Conversion Optimization at Ximalaya

Ximalaya Technology Team

Dec 21, 2023 · Frontend Development

SSR and SEO Optimization Practices for Next.js 13 with Tailwind CSS

The guide shows how to use Next.js 13’s server‑side rendering with TypeScript and Tailwind CSS to generate fully rendered HTML for better SEO, recommending direct output of key content, native anchor tags for navigation, proper TDK, robots.txt, sitemap.xml, async components, CDN asset prefixes, optimized Tailwind utilities, and careful image handling.

Next.jsSEOSSR

0 likes · 13 min read

SSR and SEO Optimization Practices for Next.js 13 with Tailwind CSS

Ximalaya Technology Team

Dec 19, 2023 · Cloud Computing

Text-Based Audio Editing in Cloud Editing: Architecture, Features, and Performance Optimizations

The article discusses cloud-based audio editing tool architecture, focusing on text‑based editing enabled by ASR, hierarchical DOM (Word, Sentence, Paragraph), performance challenges with massive character nodes, and optimizations like viewport‑based rendering and efficient drag‑select, achieving large speed gains for long recordings.

ASRPerformance OptimizationText Editing

0 likes · 14 min read

Text-Based Audio Editing in Cloud Editing: Architecture, Features, and Performance Optimizations