recommenders

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model.summary() report errors

1

Hi thanks for writing this amazing package! I'm trying to follow the tutorial codes for item retrieval: https://www.tensorflow.org/recommenders/examples/basic_retrieval After fitting the model, I'm trying to see the detail information of...

silver1886

[Question] Batch metrics and Top K Factorized do not correlate.

1

Hi! I have a retrieval model which is showing the following behaviour: - loss is reducing - top k factorized is falling (degrading) - top k batch metric is improving...

ydennisy

Similar dataset but recommendations always the same for all users

32

Having successfully got my data into the [correct format](https://github.com/tensorflow/recommenders/issues/315) I've been able to run the quickstart recommender on my own dataset. However for some reason on a given run every...

tansaku

[tfrs.tasks.Retrieval](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/tasks/Retrieval) uses the [tf.keras.losses.CategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy) loss function by default. I think that the number of classes in [tf.keras.losses.CategoricalCrossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/CategoricalCrossentropy) equals the batch size when we don't set the num_hard_negatives. Usually, the batch...

rayuron

[Question] Examples of Retrieval and Ranking Models in a Single Sequential Model?

5

Using TensorFlow Recommenders, we often employ a standard process: 1. Retrieve candidates using a retrieval model. 2. "Explode" the retrieval queries and retrieved candidates into query-candidate pairs to be ranked....

rlcauvin

Model is not computing metrics after recompiling in efficient serving tutorial

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I am running the code in efficient_serving tutorial. On evaluating for first time, model is computing metrics and losses. But after adding Brute force layer or scann layer, recompiling and...

kiranklc

RemoveAccidentalHits Not Work Correctly

8

First i will explain code I took the code here and analyzed it. [class RemoveAccidentalHits(tf.keras.layers.Layer)](https://github.com/tensorflow/recommenders/blob/1e45dd47dba48d31d95839a196eace39a9fc9d56/tensorflow_recommenders/layers/loss.py#L114C1-L115C1) ``` import tensorflow as tf # MIN_FLOAT MIN_FLOAT = 1e-8 # Örnek veri oluştur batch_size...

mustfkeskin

Incremental retrieval model training with Hashing method

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I have developed a retrieval model for personalized movie recommendations. However, in the real world, new users and new content continue to emerge. To address this challenge, I have learned...

nicewenhui

Approach for Small Number of Items in Catalog

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Hi, my training data involves implicit feedback from users, specifically their past purchase history (no ratings). Our item catalog is small, around 20 items. The goal is to rank these...

chiragdaryani

How to read the FactorizedTopK metric?

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factorized_top_k/top_1_categorical_accuracy: 0.0025 - factorized_top_k/top_5_categorical_accuracy: 0.0200 - factorized_top_k/top_10_categorical_accuracy: 0.0379 - factorized_top_k/top_50_categorical_accuracy: 0.1261 - factorized_top_k/top_100_categorical_accuracy: 0.1962 How should we read these numbers? What are the numbers to be considered as a good...

zhifeng-huang