LLMSearchRecommender README

The project's README from github.com/alopatenko/LLMSearchRecommender. As one markdown file: README.md.

Awesome Generative AI in Search, Recommendation, Personalization

Generative AI and LLMs for Search, Recommender, Personalization Engines

The goal of this repository is to survey and review generative AI and LLM-based methods for building large-scale search and recommender engines. see also LLM Evaluation methods repository

Table of Content

🔍 LLM, Search & Recommender Engines

Chart of Search AI

📚 Foundations

🎯 Core Capabilities

🧩 RAG & Retrieval

📊 Ranking & Embeddings

🧠 Response Generation & Deep Research

🎬 Recommender Systems

✅ Evaluation

🏢 Verticals

Search Surveys

Recommender Engine Surveys

Conferences, Workshops

Industrial conferences

Tutorials

Software, libraries, frameworks

also see Evaluation Agentic Search

The section should be rewritten, there are a lot of changes from FreshLLM time

Search Assistance

autocomplete/autosuggest and other search assistance tasks, search clarification, query recommendation and other techniques guiding users in search

Multi Turn

Task Solving

Personalization

Multi modal

Multi Lingual

Cross Language Information Retrieval (CLIR) and other technique to make search engines multi lingual Also, see several multi lingual benchmarks and multi lingual embedding models in the other parts of this survey

Question Answering

Querying Structured Information

RAG

Knowledge Graphs and RAG

Industrial blog articles

Retrieval

and Recommendations

Classical bi-encoder and cross encoder ranking,

bert based ranking, hybrid encoder based ranking

Reranking

Query Understanding

Embedding models

Embedding models evaluation

Optimization of embedding models

Embedding training

Finetuning embedding models

Document understanding

Response Generation

Deep Research

and Deep Search Also see Evaluation Deep Research

Prevention of hallucinations in Deep Research

Recommender Engines

TODO to classify

Sequential Recommendation

Discovery

Unclassified

methods (unclassified. TODO classify). methods used in search engines

Recommender Rankers

Industrial approaches

Evaluation of Search engines

Evaluation of RAG

and Question Answering and knowledge assistants and information seeking LLM based systems

Evaluation Deep Research

Evaluation Reasoning and RAG

QA Benchmarks

QA is used in many vertical domains, see Vertical section bellow

Blog posts, whitepapers

Verticals

Location Aware (Maps, real estate, local. travel)

Ads / advertisement

Real estate

Healthcare

Science

Finance

Search Engine Optimization

Search Engine Optimization, adversial

Job

Maintenance, Repair, Manufacturing

OLD TOC

LLMSearchRecommender (alopatenko/LLMSearchRecommender)

Searching Database…

这是什么?一份精心整理的汇编,收录关于使用生成式 AI 和大语言模型构建搜索引擎、推荐系统、个性化引擎和问答系统的研究论文、综述和行业工程实践。
这是一个单一列表,还是按主题组织的索引?它是一个结构化、按主题组织的索引:README 分为若干主要章节,例如 Agentic & Conversational Search、RAG & Retrieval、Ranking & Embeddings、Recommender Systems、Evaluation 和行业垂直领域,每一节都指向论文、综述、教程和博客文章。
这个列表是怎么组织的?我应该从哪里开始?从目录开始:Foundations 章节提供宽泛的综述和会议指引,然后跳进任何符合你兴趣的应用章节(Agentic Search、RAG & Retrieval、Ranking & Embeddings 或 Recommender Systems),再到 Evaluation 查看相关基准测试。
所有章节的内容都一样充实吗?不是。大多数章节都密密麻麻收录了到 2026 年的带日期条目,但有少数垂直领域被明确标记为不完整,例如 Finance(金融)章节。
这份汇编是给谁看的?面向构建或研究基于大语言模型的搜索、推荐、个性化和问答系统的研究人员和工程师,他们希望有一张涵盖综述、论文、会议和行业文章的统一地图。
有没有配套的大语言模型评估方法仓库?有,README 指向一个单独的“LLM Evaluation methods”仓库,专门提供给需要评估技术而非搜索或推荐系统方法的读者。
它包含行业文章,还是只有学术论文?两者都有:除了 arxiv 论文和综述之外,它还链接 Netflix、Pinterest、Airbnb、Amazon 和 Anthropic 等公司的行业博客文章和白皮书。
它覆盖哪些行业垂直领域?它有专门的子章节覆盖产品搜索、地图/房地产/旅行、广告、医疗、科学、金融、法律、求职、SEO,以及 MRO(维护、维修、大修)。
这个列表也追踪即将召开的会议吗?是的,它列出了 SIGIR、RecSys、KDD、WWW、Haystack 和 ElasticON 等学术和工业界会议及研讨会,包括排期到 2026 年的场次。
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Commits · 90 Days