REPOGEO REPORT · LITE

decodingai-magazine/personalized-recommender-course

Default branch main · commit eb974282 · scanned 6/11/2026, 10:13:27 PM

GitHub: 645 stars · 133 forks

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

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 decodingai-magazine/personalized-recommender-course, 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.

OVERALL DIRECTION

highreadme#1
Reposition the README H1 to specify it's a course for building and deploying systems
Why:
CURRENT
```
Hands-on H&M Real-Time Personalized Recommender
```
COPY-PASTE FIX
```
Hands-on Course: Build & Deploy a Real-Time Personalized Recommender System
```
highhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://www.decodingai.com/t/personalized-recommender
```

mediumtopics#3

Expand topics to include system-level keywords

Why:

CURRENT

course, feature-store, mlops, personalized-recommendation, python, recommender-system, tutorial, two-tower-neural-networks

COPY-PASTE FIX

course, feature-store, mlops, personalized-recommendation, python, recommender-system, tutorial, two-tower-neural-networks, real-time, deployment, architecture, system-design

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.

Recall

0 / 2

0% of queries surface decodingai-magazine/personalized-recommender-course

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

apache/spark

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

apache/spark · recommended 2×
apache/kafka · recommended 1×
apache/flink · recommended 1×
redis/redis · recommended 1×
tensorflow/tensorflow · recommended 1×

CATEGORY QUERY
How to architect and deploy a real-time personalized recommender system using modern ML practices?
you: not recommended
AI recommended (in order):
1. Apache Kafka (apache/kafka)
2. Apache Flink (apache/flink)
3. Apache Spark Streaming (apache/spark)
4. Redis (redis/redis)
5. TensorFlow (tensorflow/tensorflow)
6. PyTorch (pytorch/pytorch)
7. MLflow (mlflow/mlflow)
8. Kubeflow (kubeflow/kubeflow)
9. TensorFlow Serving (tensorflow/serving)
10. TorchServe (pytorch/serve)
11. Faiss (facebookresearch/faiss)
12. Kubernetes (kubernetes/kubernetes)
13. Docker
14. Apache Spark (apache/spark)
15. S3
16. HDFS (apache/hadoop)
AI recommended 16 alternatives but never named decodingai-magazine/personalized-recommender-course. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a hands-on course to build personalized recommendation engines with neural networks.
you: not recommended
AI recommended (in order):
1. DeepLearning.AI's Deep Learning Specialization
2. Coursera's Recommender Systems Specialization
3. Udemy's Build a Recommendation Engine with Deep Learning in Python
4. fast.ai's Practical Deep Learning for Coders
5. fastai (fastai/fastai)
6. edX's Applied AI with DeepLearning
AI recommended 6 alternatives but never named decodingai-magazine/personalized-recommender-course. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

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 decodingai-magazine/personalized-recommender-course?
pass
AI did not name decodingai-magazine/personalized-recommender-course — 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 decodingai-magazine/personalized-recommender-course in production, what risks or prerequisites should they evaluate first?
pass
AI named decodingai-magazine/personalized-recommender-course 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 decodingai-magazine/personalized-recommender-course solve, and who is the primary audience?
pass
AI did not name decodingai-magazine/personalized-recommender-course — 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 decodingai-magazine/personalized-recommender-course. 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

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/decodingai-magazine/personalized-recommender-course.svg)](https://repogeo.com/en/r/decodingai-magazine/personalized-recommender-course)

HTML

<a href="https://repogeo.com/en/r/decodingai-magazine/personalized-recommender-course"><img src="https://repogeo.com/badge/decodingai-magazine/personalized-recommender-course.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

decodingai-magazine/personalized-recommender-course — 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