Search and Recommender Engines AI
Rethinking LLM-based Query Expansion, Apr 2025, arxiv Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling. A Reproducibility Study, Sep 2026, arxiv RecGPT: A User Intent-Centric Next-Generation LLM-Powered Recommender System in Industrial Practice, Aug 2026, ACM Transactions on Information Systems Who Are We Recommending To? 17 domains, like legal, religious, programming, web, social, medical, blog, academic, etc.
- βͺRethinking LLM-based Query Expansion, Apr 2025, arxiv Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling.
- βͺA Reproducibility Study, Sep 2026, arxiv RecGPT: A User Intent-Centric Next-Generation LLM-Powered Recommender System in Industrial Practice, Aug 2026, ACM Transactions on Information Systems Who Are We Recommending To?
- βͺ17 domains, like legal, religious, programming, web, social, medical, blog, academic, etc.
2 outlets in our directory ran this story, first to last over 18 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- βͺ For MariaDB, LLMs are the new search engines to win over β The Register
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,391 of its stories.
Story provenance
Source Β· retrieval Β· rights Β· ranking β open for full record
inspect β
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/alopatenko/LLMSearchRecommender |
| Publication time | Fri, 25 Sep 2026 03:27:08 +0000 |
| Retrieval time | 2026-09-25T03:45:31.188Z |
| Last seen | 2026-09-25T03:45:31.188Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch Β· cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | drOQe8YASPIE Β· 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes β open original |
| Substitutes article? | No β link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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 π LLM, Search & Recommender Engines π Foundations Search Surveys β broad reviews of AI-driven search Recommender Engine Surveys β reviews of LLM-powered recommendation methods Conferences, Workshops β SIGIR, RecSys, KDD, WWW and other venues, top conferences for search and recommendation Industrial Conferences β Industrial events such as Haystack and Activate practitioner events Tutorials β Tutorials Software, Libraries, Frameworks β open-source deepβ¦
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.