REPOGEO REPORT · LITE
daicoolb/RecommenderSystem-Paper
Default branch master · commit 022002cf · scanned 5/30/2026, 5:12:39 PM
GitHub: 744 stars · 204 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 daicoolb/RecommenderSystem-Paper, 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 to emphasize it's a curated paper collection
Why:
CURRENT## Papers, tools , and framewroks that used in Recommender System For the convenience of reading, I collect some basic and important papers about recommender system.
COPY-PASTE FIX## Curated Reading List: Foundational & Interesting Papers in Recommender Systems This repository serves as a personal, curated collection of foundational and interesting research papers on recommender systems, including those I've read or plan to explore. It's designed to help researchers and students navigate key literature in the field.
- highlicense#2Add a standard open-source LICENSE file
Why:
CURRENT(no LICENSE file detected)
COPY-PASTE FIXCreate a LICENSE file (e.g., MIT License or Apache-2.0) in the repository root to clearly state the terms of use for the collected papers and repository content.
- mediumhomepage#3Set the repository URL as the homepage in the 'About' section
Why:
COPY-PASTE FIXIn the repository's 'About' section, set the homepage URL to: https://github.com/daicoolb/RecommenderSystem-Paper
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.
- Neural Collaborative Filtering (NCF) · recommended 1×
- Wide & Deep Learning for Recommender Systems · recommended 1×
- Deep Learning for Recommender Systems: A Survey of the State-of-the-Art · recommended 1×
- AutoRec: Autoencoders Meet Collaborative Filtering · recommended 1×
- Variational Autoencoders for Collaborative Filtering · recommended 1×
- CATEGORY QUERYWhere can I find foundational research papers on deep learning for building recommender systems?you: not recommendedAI recommended (in order):
- Neural Collaborative Filtering (NCF)
- Wide & Deep Learning for Recommender Systems
- Deep Learning for Recommender Systems: A Survey of the State-of-the-Art
- AutoRec: Autoencoders Meet Collaborative Filtering
- Variational Autoencoders for Collaborative Filtering
- Deep Neural Networks for YouTube Recommendations
- Recurrent Neural Networks for Session-based Recommendation
AI recommended 7 alternatives but never named daicoolb/RecommenderSystem-Paper. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the latest research papers addressing the cold start problem in recommendation engines?you: not recommendedAI recommended (in order):
- BERT
- GPT
AI recommended 2 alternatives but never named daicoolb/RecommenderSystem-Paper. 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 daicoolb/RecommenderSystem-Paper?passAI did not name daicoolb/RecommenderSystem-Paper — 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 daicoolb/RecommenderSystem-Paper in production, what risks or prerequisites should they evaluate first?passAI did not name daicoolb/RecommenderSystem-Paper — 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?
- In one sentence, what problem does the repo daicoolb/RecommenderSystem-Paper solve, and who is the primary audience?passAI did not name daicoolb/RecommenderSystem-Paper — 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
Drop this badge into the README of daicoolb/RecommenderSystem-Paper. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/daicoolb/RecommenderSystem-Paper)<a href="https://repogeo.com/en/r/daicoolb/RecommenderSystem-Paper"><img src="https://repogeo.com/badge/daicoolb/RecommenderSystem-Paper.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
daicoolb/RecommenderSystem-Paper — 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