mirror of
https://github.com/openmlsys/openmlsys-zh.git
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d953030747
* feat: add v1/v2 versioning and language selector for mdbook - Copy current content to v1/ directory (1st Edition) - Create v2/ directory with new TOC structure (2nd Edition) and placeholder chapters - Add version selector (V1/V2) and language toggle (EN/ZH) in top-right nav bar - Add build scripts: build_mdbook_v1.sh, build_mdbook_v2.sh - Update assemble_docs_publish_tree.py to support v1/v2 deployment layout - Fix mdbook preprocessor to use 'sections' key (v0.4.43 compatibility) - Update .gitignore for new build artifact directories - Deployment layout: / = v2 EN, /cn/ = v2 ZH, /v1/ = v1 EN, /v1/cn/ = v1 ZH Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * build: update CI to build and verify all four books (v1/v2 x EN/ZH) - Clarify step names: "Build v2 (EN + ZH)" and "Build v1 (EN + ZH)" - Add verification step to check all four index.html outputs exist - Deploy workflow assembles: / = v2 EN, /cn/ = v2 ZH, /v1/ = v1 EN, /v1/cn/ = v1 ZH Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: gracefully skip missing TOC entries instead of crashing resolve_toc_target() now returns None for missing files instead of raising FileNotFoundError. This fixes v1 EN build where chapter index files reference TOC entry names that don't match actual filenames. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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深度学习推荐系统
推荐模型通过对用户特征、物品特征、用户-物品历史交互行为等数据的分析，为用户推荐可能感兴趣的内容、商品或者广告1 。在信息爆炸的时代，高效且准确的推荐结果能够极大地提升用户在使用服务时的体验。近年来，基于深度学习的推荐模型2 由于可以高效地从海量数据中发掘用户的潜在兴趣，被谷歌、脸书、阿里巴巴等各大公司广泛应用于生产环境中。为了支持推荐模型的稳定高质量服务，人们围绕其搭建了一系列组件，这些组件和推荐模型共同构成了庞大而又精巧的深度学习推荐系统。本章主要介绍以深度学习模型为中心的推荐系统的基本组成、运行原理以及其在在线环境中面临的挑战和对应的解决方案。
:maxdepth: 2
system_architecture
multi_stage_recommender_system
model_update
case_study
summary