REPOGEO REPORT · LITE

tensorflow/recommenders-addons

Default branch master · commit b3bc3d46 · scanned 6/14/2026, 3:07:42 AM

GitHub: 636 stars · 144 forks

AI VISIBILITY SCORE

28 /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

2 / 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 tensorflow/recommenders-addons, 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 README opening to highlight advanced dynamic embedding capabilities

Why:

CURRENT

TensorFlow Recommenders Addons(TFRA) are a collection of projects related to large-scale recommendation systems built upon TensorFlow by introducing the **Dynamic Embedding Technology** to TensorFlow that makes TensorFlow more suitable for training models of **Search, Recommendations, and Advertising** and makes building, evaluating, and serving sophisticated recommenders models easy.

COPY-PASTE FIX

TensorFlow Recommenders Addons (TFRA) significantly extends TensorFlow's capabilities for **large-scale recommendation systems** by providing **advanced Dynamic Embedding Technology**. TFRA offers **trainable key-value data structures** that go beyond standard TensorFlow embeddings and lookup tables, making it uniquely suitable for building, evaluating, and serving sophisticated models in **Search, Recommendations, and Advertising** with unparalleled scale and performance.

mediumhomepage#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://pypi.org/project/tensorflow-recommenders-addons/
```

lowtopics#3

Add more specific topics to improve categorization

Why:

CURRENT

dynamic-embedding, recommender-system, sig-recommenders, tensorflow, tensorflow-recommenders-addons

COPY-PASTE FIX

dynamic-embedding, recommender-system, sig-recommenders, tensorflow, tensorflow-recommenders-addons, large-scale, key-value-store, trainable-embeddings, deep-learning-recommendations

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 tensorflow/recommenders-addons

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

tf.lookup.StaticHashTable

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

tf.lookup.StaticHashTable · recommended 1×
tf.lookup.KeyValueTensorInitializer · recommended 1×
tf.keras.layers.Embedding · recommended 1×
tensorflow/recommenders · recommended 1×
tf.lookup.MutableHashTable · recommended 1×

CATEGORY QUERY
How to implement dynamic embedding layers for large-scale recommendation systems in TensorFlow?
you: not recommended
AI recommended (in order):
1. tf.lookup.StaticHashTable
2. tf.lookup.KeyValueTensorInitializer
3. tf.keras.layers.Embedding
4. TensorFlow Recommenders (tensorflow/recommenders)
5. tf.lookup.MutableHashTable
6. tf.data.experimental.make_embedding_dataset
7. TensorFlow Extended (TFX) (tensorflow/tfx)
8. tf.Transform
9. TensorFlow Serving (tensorflow/serving)
AI recommended 9 alternatives but never named tensorflow/recommenders-addons. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools enable trainable key-value embeddings for improved recommendation model performance?
you: not recommended
AI recommended (in order):
1. TensorFlow
2. PyTorch
3. LightFM
4. DeepCTR
5. Spark MLlib
6. Hugging Face Transformers
AI recommended 6 alternatives but never named tensorflow/recommenders-addons. 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 tensorflow/recommenders-addons?
pass
AI named tensorflow/recommenders-addons explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts tensorflow/recommenders-addons in production, what risks or prerequisites should they evaluate first?
pass
AI named tensorflow/recommenders-addons 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 tensorflow/recommenders-addons solve, and who is the primary audience?
pass
AI did not name tensorflow/recommenders-addons — 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 tensorflow/recommenders-addons. 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/tensorflow/recommenders-addons.svg)](https://repogeo.com/en/r/tensorflow/recommenders-addons)

HTML

<a href="https://repogeo.com/en/r/tensorflow/recommenders-addons"><img src="https://repogeo.com/badge/tensorflow/recommenders-addons.svg" alt="RepoGEO" /></a>

Pro

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite