REPOGEO REPORT · LITE
WLiK/LLM4Rec-Awesome-Papers
Default branch main · commit 8b04df73 · scanned 5/9/2026, 6:23:22 PM
GitHub: 2,274 stars · 164 forks
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface WLiK/LLM4Rec-Awesome-Papers, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Clarify the README's opening sentence to emphasize it's a curated list
Why:
CURRENTA list of awesome papers and resources of recommender system on large language model (LLM).
COPY-PASTE FIXA curated and continuously updated list of awesome papers and resources specifically focused on the intersection of recommender systems and large language models (LLMs).
- highlicense#2Add a LICENSE file to the repository
Why:
COPY-PASTE FIXCreate a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0) to clarify usage terms for contributors and users.
- mediumhomepage#3Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIXSet the 'Homepage' URL in the repository's About section to `https://arxiv.org/abs/2305.19860` (or the most relevant project/survey page) to provide a direct link to the associated survey.
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- Google Scholar · recommended 1×
- arXiv · recommended 1×
- ACM Digital Library · recommended 1×
- IEEE Xplore Digital Library · recommended 1×
- Semantic Scholar · recommended 1×
- CATEGORY QUERYWhere can I find academic papers on using large language models for personalized recommendations?you: not recommendedAI recommended (in order):
- Google Scholar
- arXiv
- ACM Digital Library
- IEEE Xplore Digital Library
- Semantic Scholar
- Microsoft Academic
- RecSys
- KDD
- WWW
- SIGIR
- NeurIPS
- ICML
- ICLR
AI recommended 13 alternatives but never named WLiK/LLM4Rec-Awesome-Papers. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the latest research trends and techniques for integrating LLMs into recommender systems?you: not recommendedAI recommended (in order):
- Sentence-BERT (SBERT)
- MPNet
- OpenAI Embeddings
- Cohere Embeddings
- GPT-3.5
- GPT-4
- Llama 2
- Mistral
- Cross-Encoders
- Claude 2
- LangChain
- Haystack
- BERT
- RoBERTa
AI recommended 14 alternatives but never named WLiK/LLM4Rec-Awesome-Papers. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of WLiK/LLM4Rec-Awesome-Papers?passAI did not name WLiK/LLM4Rec-Awesome-Papers — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts WLiK/LLM4Rec-Awesome-Papers in production, what risks or prerequisites should they evaluate first?passAI named WLiK/LLM4Rec-Awesome-Papers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo WLiK/LLM4Rec-Awesome-Papers solve, and who is the primary audience?passAI did not name WLiK/LLM4Rec-Awesome-Papers — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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WLiK/LLM4Rec-Awesome-Papers — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite