recommenders

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Fix to restore from SC checkpoints

1

Fix to restore from SC checkpoints

copybara-service[bot]

Hello. Thank you for this great lib and all the learning here! I am wondering if anyone has experienced the following error when trying to distribute TF Recommenders with Horovod....

BrianMiner

Fix typo in featurization.ipynb

2

vidhant

How to know the Rank model improves the output of the Retrieval model in the case of MovieLens dataset?

15

We all know that the rank will enhance the output of the retrieval model. But how can we see that in the case of MovieLens dataset?

houghtonweihu

how to return NDCG as a metric

2

Hello, I am following the tutorial here https://www.tensorflow.org/recommenders/examples/dcn to build a dcn model, but I want to have ndcg as a metric. Then I did `model.compile(optimizer='sgd', metrics=[tfr.keras.metrics.NDCGMetric()]) ` but still...

Lulu20220

candidates argument for FactorizedTopK

3

Hi, `metrics = tfrs.metrics.FactorizedTopK( candidates=movies.batch(128).map(movie_model) )` I'm trying to figure out how 'candidates' argument works for FactorizedTopK metric from the retrieval tutorial. The tutorial uses 'movies' dataset, and I found...

datasciyj

[Question] Candidate sampling probability for Mixed Negative Sampling

17

Hi Team, Sorry for posting many questions, I hope this will help others navigate through building high quality models :) I've trained a two tower model, and checking the possibility...

OmarMAmin

How to know better evaluation metrics for Retrieval or Rank will translate into better actual recommendations in the case of MovieLens dataset?

2

In the tutorials of Tensorflow Recommenders, top_k_categorical_accuracy is used for the evaluation of Retrieval, and mse for Rank. Do we have examples that show better evaluation metrics translate into better...

houghtonweihu

invalid results on during evaluation

3

Hi, I am following this tutorial https://www.tensorflow.org/recommenders/examples/basic_retrieval . during model.fit and model.evaluate i am passing the same dataset but i am getting very different numbers like below. after model.evaluate results:...

riyaj8888

i have tensorflow==2.4.0 and when i am trying to install tfrs using pip install tensorflow-recommenders ,its automatically installing tensorflow=2.13.1 which i don't want.

riyaj8888