recommenders

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[Question] Ranking returning always same order

3

hello, there! I was exploring the [basic ranking tutorial](https://www.tensorflow.org/recommenders/examples/basic_ranking) and it seems that, for a fixed set of items, the ranking values vary very little when comparing different users and...

giovana-morais

Hi! So right now, I'm creating an item-to-item recommender system that uses item numbers and item titles as features. One tower of my model focuses on "item A" and the...

nikhil-krishna

Enable tfrs models to handle non-scalar regularization losses by applying reduce_sum instead of sum on regularization losses.

1

Enable tfrs models to handle non-scalar regularization losses by applying reduce_sum instead of sum on regularization losses.

copybara-service[bot]

Seeking Guidance on Feature Preparation for Retrieval and Ranking Models

10

Hello, I am currently working on developing a recommender system for internal customers and have found this framework to be a great tool to start with. As a beginner in...

dfangish

Explainability of TFRS-retrieval model

2

We are using TFRS-retrieval solution for our problem statement. Now we are looking for the model explainability part of TFRS-retrieval model. is there any way we can backtrack from the...

drtinumohan

How to use pertained embedding as feature in the user model

5

Hello, I have a signature embedding of size 256 for each user. How can I use this vector as a feature in the user model? Any tips are appreciated.

zhifeng-huang

Interpretation of scores and how to find the scores confidence level?

1

Hello there, 1- I am wondering if the user-item scores (ie query-candidate scores) outputted by model prediction are interaction-probability or affinity scores? 2- Is it possible to compare the scores...

zahraegh

How does regularisation affect the training speed? I am getting X100 boost.

4

Hello! I am experimenting with the base model from tutorials. I have noted that after adding `tf.keras.regularizers.l2(0.05) )` to embeddings layer two things happen: 1. Metrics start growing - understandable....

IgorHoholko

Fix: extra negatives causes shape mismatch in FactorizedTopK metrics

1

### The Problem The Retrieval task already slices the `candidate_embeddings` tensor to remove extra negatives, but it doesn't do the same for the `candidate_ids`. This leads to a shape mismatch...

patrickorlando

[Question] How to use Attention to aggregate history?

2

@OmarMAmin mentioned [Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model](https://assets.amazon.science/1e/e6/4d7f8a2741a4a3b148e20a953946/exploring-heterogeneous-metadata-for-video-recommendation-with-two-tower-model.pdf) paper in [this discussion](https://github.com/tensorflow/recommenders/issues/633). I read through the paper multiple times and a few resources about existing Attention mechanisms,...

hkristof03