GitHub Repository: keras-team/keras-io
Path: blob/master/templates/examples/keras_rs/sequential_retrieval.md
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Sequential retrieval [GRU4Rec]

Author: Abheesht Sharma, Fabien Hertschuh
Date created: 2025/04/28
Last modified: 2025/04/28
Description: Recommend movies using a GRU-based sequential retrieval model.

ⓘ This example uses Keras 2

[**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/keras_rs/ipynb/sequential_retrieval.ipynb) •

[**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/keras_rs/sequential_retrieval.py)

Introduction

In this example, we are going to build a sequential retrieval model. Sequential recommendation is a popular model that looks at a sequence of items that users have interacted with previously and then predicts the next item. Here, the order of the items within each sequence matters. So, we are going to use a recurrent neural network to model the sequential relationship. For more details, please refer to the GRU4Rec paper.

Let's begin by choosing JAX as the backend we want to run on, and import all the necessary libraries.

import os

os.environ["KERAS_BACKEND"] = "jax"  # `"tensorflow"`/`"torch"`

import collections
import os
import random

import keras
import pandas as pd
import tensorflow as tf  # Needed only for the dataset

import keras_rs

Let's also define all important variables/hyperparameters below.

DATA_DIR = "./raw/data/"

# MovieLens-specific variables
MOVIELENS_1M_URL = "https://files.grouplens.org/datasets/movielens/ml-1m.zip"
MOVIELENS_ZIP_HASH = "a6898adb50b9ca05aa231689da44c217cb524e7ebd39d264c56e2832f2c54e20"

RATINGS_FILE_NAME = "ratings.dat"
MOVIES_FILE_NAME = "movies.dat"

# Data processing args
MAX_CONTEXT_LENGTH = 10
MIN_SEQUENCE_LENGTH = 3
TRAIN_DATA_FRACTION = 0.9

RATINGS_DATA_COLUMNS = ["UserID", "MovieID", "Rating", "Timestamp"]
MOVIES_DATA_COLUMNS = ["MovieID", "Title", "Genres"]
MIN_RATING = 2

# Training/model args
BATCH_SIZE = 4096
TEST_BATCH_SIZE = 2048
EMBEDDING_DIM = 32
NUM_EPOCHS = 5
LEARNING_RATE = 0.05

Dataset

Next, we need to prepare our dataset. Like we did in the basic retrieval example, we are going to use the MovieLens dataset.

The dataset preparation step is fairly involved. The original ratings dataset contains (user, movie ID, rating, timestamp) tuples (among other columns, which are not important for this example). Since we are dealing with sequential retrieval, we need to create movie sequences for every user, where the sequences are ordered by timestamp.

Let's start by downloading and reading the dataset.

# Download the MovieLens dataset.
if not os.path.exists(DATA_DIR):
    os.makedirs(DATA_DIR)

path_to_zip = keras.utils.get_file(
    fname="ml-1m.zip",
    origin=MOVIELENS_1M_URL,
    file_hash=MOVIELENS_ZIP_HASH,
    hash_algorithm="sha256",
    extract=True,
    cache_dir=DATA_DIR,
)
movielens_extracted_dir = os.path.join(
    os.path.dirname(path_to_zip),
    "ml-1m_extracted",
    "ml-1m",
)


# Read the dataset.
def read_data(data_directory, min_rating=None):
    """Read movielens ratings.dat and movies.dat file
    into dataframe.
    """

    ratings_df = pd.read_csv(
        os.path.join(data_directory, RATINGS_FILE_NAME),
        sep="::",
        names=RATINGS_DATA_COLUMNS,
        encoding="unicode_escape",
    )
    ratings_df["Timestamp"] = ratings_df["Timestamp"].apply(int)

    # Remove movies with `rating < min_rating`.
    if min_rating is not None:
        ratings_df = ratings_df[ratings_df["Rating"] >= min_rating]

    movies_df = pd.read_csv(
        os.path.join(data_directory, MOVIES_FILE_NAME),
        sep="::",
        names=MOVIES_DATA_COLUMNS,
        encoding="unicode_escape",
    )
    return ratings_df, movies_df


ratings_df, movies_df = read_data(
    data_directory=movielens_extracted_dir, min_rating=MIN_RATING
)

# Need to know #movies so as to define embedding layers.
movies_count = movies_df["MovieID"].max()

``` Downloading data from https://files.grouplens.org/datasets/movielens/ml-1m.zip

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:26: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'. ratings_df = pd.read_csv(

:38: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'. movies_df = pd.read_csv(

</div>
Now that we have read the dataset, let's create sequences of movies
for every user. Here is the function for doing just that.


```python

def get_movie_sequence_per_user(ratings_df):
    """Get movieID sequences for every user."""
    sequences = collections.defaultdict(list)

    for user_id, movie_id, rating, timestamp in ratings_df.values:
        sequences[user_id].append(
            {
                "movie_id": movie_id,
                "timestamp": timestamp,
                "rating": rating,
            }
        )

    # Sort movie sequences by timestamp for every user.
    for user_id, context in sequences.items():
        context.sort(key=lambda x: x["timestamp"])
        sequences[user_id] = context

    return sequences

We need to do some filtering and processing before we proceed with training the model:

Form sequences of all lengths up to min(user_sequence_length, MAX_CONTEXT_LENGTH). So, every user will have multiple sequences corresponding to it.
Get labels, i.e., Given a sequence of length n, the first n-1 tokens will be fed to the model as input, and the label will be the last token.
Remove all user sequences with less than MIN_SEQUENCE_LENGTH movies.
Pad all sequences to MAX_CONTEXT_LENGTH.


def generate_examples_from_user_sequences(sequences):
    """Generates sequences for all users, with padding, truncation, etc."""

    def generate_examples_from_user_sequence(sequence):
        """Generates examples for a single user sequence."""

        examples = []
        for label_idx in range(1, len(sequence)):
            start_idx = max(0, label_idx - MAX_CONTEXT_LENGTH)
            context = sequence[start_idx:label_idx]

            # Padding
            while len(context) < MAX_CONTEXT_LENGTH:
                context.append(
                    {
                        "movie_id": 0,
                        "timestamp": 0,
                        "rating": 0.0,
                    }
                )

            label_movie_id = int(sequence[label_idx]["movie_id"])
            context_movie_id = [int(movie["movie_id"]) for movie in context]

            examples.append(
                {
                    "context_movie_id": context_movie_id,
                    "label_movie_id": label_movie_id,
                },
            )
        return examples

    all_examples = []
    for sequence in sequences.values():
        if len(sequence) < MIN_SEQUENCE_LENGTH:
            continue

        user_examples = generate_examples_from_user_sequence(sequence)

        all_examples.extend(user_examples)

    return all_examples

Let's split the dataset into train and test sets. Also, we need to change the format of the dataset dictionary so as to enable conversion to a tf.data.Dataset object.

sequences = get_movie_sequence_per_user(ratings_df)
examples = generate_examples_from_user_sequences(sequences)

# Train-test split.
random.shuffle(examples)
split_index = int(TRAIN_DATA_FRACTION * len(examples))
train_examples = examples[:split_index]
test_examples = examples[split_index:]


def list_of_dicts_to_dict_of_lists(list_of_dicts):
    """Convert list of dictionaries to dictionary of lists for
    `tf.data` conversion.
    """
    dict_of_lists = collections.defaultdict(list)
    for dictionary in list_of_dicts:
        for key, value in dictionary.items():
            dict_of_lists[key].append(value)
    return dict_of_lists


train_examples = list_of_dicts_to_dict_of_lists(train_examples)
test_examples = list_of_dicts_to_dict_of_lists(test_examples)

train_ds = tf.data.Dataset.from_tensor_slices(train_examples).map(
    lambda x: (x["context_movie_id"], x["label_movie_id"])
)
test_ds = tf.data.Dataset.from_tensor_slices(test_examples).map(
    lambda x: (x["context_movie_id"], x["label_movie_id"])
)

We need to batch our datasets. We also user cache() and prefetch() for better performance.

train_ds = train_ds.batch(BATCH_SIZE).cache().prefetch(tf.data.AUTOTUNE)
test_ds = test_ds.batch(TEST_BATCH_SIZE).cache().prefetch(tf.data.AUTOTUNE)

Let's print out one batch.

for sample in train_ds.take(1):
    print(sample)

``` (, )

</div>
---
## Model and Training

In the basic retrieval example, we used one query tower for the
user, and the candidate tower for the candidate movie. We are
going to use a two-tower architecture here as well. However,
we use the query tower with a Gated Recurrent Unit (GRU) layer
to encode the sequence of historical movies, and keep the same
candidate tower for the candidate movie.

Note: Take a look at how the labels are defined. The label tensor
(of shape `(batch_size, batch_size)`) contains one-hot vectors. The idea
is: for every sample, consider movie IDs corresponding to other samples in
the batch as negatives.


```python

class SequentialRetrievalModel(keras.Model):
    """Create the sequential retrieval model.

    Args:
      movies_count: Total number of unique movies in the dataset.
      embedding_dimension: Output dimension for movie embedding tables.
    """

    def __init__(
        self,
        movies_count,
        embedding_dimension=128,
        **kwargs,
    ):
        super().__init__(**kwargs)
        # Our query tower, simply an embedding table followed by
        # a GRU unit. This encodes sequence of historical movies.
        self.query_model = keras.Sequential(
            [
                keras.layers.Embedding(movies_count + 1, embedding_dimension),
                keras.layers.GRU(embedding_dimension),
            ]
        )

        # Our candidate tower, simply an embedding table.
        self.candidate_model = keras.layers.Embedding(
            movies_count + 1, embedding_dimension
        )

        # The layer that performs the retrieval.
        self.retrieval = keras_rs.layers.BruteForceRetrieval(k=10, return_scores=False)
        self.loss_fn = keras.losses.CategoricalCrossentropy(
            from_logits=True,
        )

    def build(self, input_shape):
        self.query_model.build(input_shape)
        self.candidate_model.build(input_shape)

        # In this case, the candidates are directly the movie embeddings.
        # We take a shortcut and directly reuse the variable.
        self.retrieval.candidate_embeddings = self.candidate_model.embeddings
        self.retrieval.build(input_shape)
        super().build(input_shape)

    def call(self, inputs, training=False):
        query_embeddings = self.query_model(inputs)
        result = {
            "query_embeddings": query_embeddings,
        }

        if not training:
            # Skip the retrieval of top movies during training as the
            # predictions are not used.
            result["predictions"] = self.retrieval(query_embeddings)
        return result

    def compute_loss(self, x, y, y_pred, sample_weight, training=True):
        candidate_id = y
        query_embeddings = y_pred["query_embeddings"]
        candidate_embeddings = self.candidate_model(candidate_id)

        num_queries = keras.ops.shape(query_embeddings)[0]
        num_candidates = keras.ops.shape(candidate_embeddings)[0]

        # One-hot vectors for labels.
        labels = keras.ops.eye(num_queries, num_candidates)

        # Compute the affinity score by multiplying the two embeddings.
        scores = keras.ops.matmul(
            query_embeddings, keras.ops.transpose(candidate_embeddings)
        )

        return self.loss_fn(labels, scores, sample_weight)

Let's instantiate, compile and train our model.

model = SequentialRetrievalModel(
    movies_count=movies_count + 1, embedding_dimension=EMBEDDING_DIM
)

# Compile.
model.compile(optimizer=keras.optimizers.AdamW(learning_rate=LEARNING_RATE))

# Train.
model.fit(
    train_ds,
    validation_data=test_ds,
    epochs=NUM_EPOCHS,
)

``` Epoch 1/5

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 207/207 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - loss: 7.3505

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 207/207 ━━━━━━━━━━━━━━━━━━━━ 8s 28ms/step - loss: 7.3487 - val_loss: 5.9852


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Epoch 2/5

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   2/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6892 

    
   3/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6883

    
   4/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6841

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   9/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6842

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  10/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6834

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  11/207 ━[37m━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6829

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  14/207 ━[37m━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6819
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  18/207 ━[37m━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6813
  16/207 ━[37m━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6816
  17/207 ━[37m━━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.6814

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  20/207 ━[37m━━━━━━━━━━━━━━━━━━━  2s 12ms/step - loss: 6.6810

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  22/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 12ms/step - loss: 6.6806
  23/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 11ms/step - loss: 6.6805

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  21/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 12ms/step - loss: 6.6808

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  25/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 12ms/step - loss: 6.6804
  24/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6803

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  26/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6804

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  27/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6804

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  31/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 12ms/step - loss: 6.6803

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  28/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6803
  29/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6803
  30/207 ━━[37m━━━━━━━━━━━━━━━━━━  2s 12ms/step - loss: 6.6803

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  32/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6802

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  38/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 12ms/step - loss: 6.6796
  36/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6795

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  34/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6799
  33/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6801

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  37/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6797

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  35/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6800

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  39/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6793
  40/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6792

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  42/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6787
  43/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6788
  41/207 ━━━[37m━━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6791

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  44/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6783

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  45/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6782

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  46/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6780
  47/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6769

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  50/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6772
  49/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 13ms/step - loss: 6.6765
  48/207 ━━━━[37m━━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6776
  51/207 ━━━━[37m━━━━━━━━━━━━━━━━  1s 13ms/step - loss: 6.6770

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  52/207 ━━━━━[37m━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6760

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  57/207 ━━━━━[37m━━━━━━━━━━━━━━━  1s 13ms/step - loss: 6.6750
  53/207 ━━━━━[37m━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6746
  54/207 ━━━━━[37m━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6756
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  58/207 ━━━━━[37m━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6737

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  63/207 ━━━━━━[37m━━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6728
  62/207 ━━━━━[37m━━━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6730
  61/207 ━━━━━[37m━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6731

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  60/207 ━━━━━[37m━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6733
  64/207 ━━━━━━[37m━━━━━━━━━━━━━━  1s 13ms/step - loss: 6.6726
  59/207 ━━━━━[37m━━━━━━━━━━━━━━━  2s 14ms/step - loss: 6.6735

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  65/207 ━━━━━━[37m━━━━━━━━━━━━━━  2s 15ms/step - loss: 6.6724

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  70/207 ━━━━━━[37m━━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6713
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  68/207 ━━━━━━[37m━━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6709
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  77/207 ━━━━━━━[37m━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6687
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  72/207 ━━━━━━[37m━━━━━━━━━━━━━━  1s 15ms/step - loss: 6.6685

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  73/207 ━━━━━━━[37m━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6699

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  74/207 ━━━━━━━[37m━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6697
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  79/207 ━━━━━━━[37m━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6672
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  83/207 ━━━━━━━━[37m━━━━━━━━━━━━  1s 14ms/step - loss: 6.6664

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  80/207 ━━━━━━━[37m━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6659
  87/207 ━━━━━━━━[37m━━━━━━━━━━━━  1s 13ms/step - loss: 6.6641

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  85/207 ━━━━━━━━[37m━━━━━━━━━━━━  1s 13ms/step - loss: 6.6640

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  78/207 ━━━━━━━[37m━━━━━━━━━━━━━  1s 14ms/step - loss: 6.6657
  84/207 ━━━━━━━━[37m━━━━━━━━━━━━  1s 13ms/step - loss: 6.6647
  86/207 ━━━━━━━━[37m━━━━━━━━━━━━  1s 13ms/step - loss: 6.6643

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 207/207 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - loss: 6.6328 - val_loss: 5.9231


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Epoch 3/5

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   1/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 10ms/step - loss: 6.5509

    
   2/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 5ms/step - loss: 6.5612 

    
   3/207 [37m━━━━━━━━━━━━━━━━━━━━  1s 5ms/step - loss: 6.5651

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  14/207 ━[37m━━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5684

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  15/207 ━[37m━━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5687

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  16/207 ━[37m━━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5688

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  17/207 ━[37m━━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5689

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  18/207 ━[37m━━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5691

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  19/207 ━[37m━━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5692

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  21/207 ━━[37m━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5694
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  25/207 ━━[37m━━━━━━━━━━━━━━━━━━  0s 5ms/step - loss: 6.5701
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  29/207 ━━[37m━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.5717
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  33/207 ━━━[37m━━━━━━━━━━━━━━━━━  1s 7ms/step - loss: 6.5723
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  27/207 ━━[37m━━━━━━━━━━━━━━━━━━  1s 8ms/step - loss: 6.5713

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  30/207 ━━[37m━━━━━━━━━━━━━━━━━━  1s 7ms/step - loss: 6.5719

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  36/207 ━━━[37m━━━━━━━━━━━━━━━━━  1s 9ms/step - loss: 6.5726
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  38/207 ━━━[37m━━━━━━━━━━━━━━━━━  1s 11ms/step - loss: 6.5730
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 207/207 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - loss: 6.5498 - val_loss: 5.9322


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Epoch 4/5

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Epoch 5/5

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  47/207 ━━━━[37m━━━━━━━━━━━━━━━━  1s 11ms/step - loss: 6.5154
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<keras.src.callbacks.history.History at 0x78645944c8d0>

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---
## Making predictions

Now that we have a model, we would like to be able to make predictions.

So far, we have only handled movies by id. Now is the time to create a mapping
keyed by movie IDs to be able to surface the titles.


```python
movie_id_to_movie_title = dict(zip(movies_df["MovieID"], movies_df["Title"]))
movie_id_to_movie_title[0] = ""  # Because id 0 is not in the dataset.

We then simply use the Keras model.predict() method. Under the hood, it calls the BruteForceRetrieval layer to perform the actual retrieval.

Note that this model can retrieve movies already watched by the user. We could easily add logic to remove them if that is desirable.

print("\n==> Movies the user has watched:")
movie_sequence = test_ds.unbatch().take(1)
for element in movie_sequence:
    for movie_id in element[0][:-1]:
        print(movie_id_to_movie_title[movie_id.numpy()], end=", ")
    print(movie_id_to_movie_title[element[0][-1].numpy()])

predictions = model.predict(movie_sequence.batch(1))
predictions = keras.ops.convert_to_numpy(predictions["predictions"])

print("\n==> Recommended movies for the above sequence:")
for movie_id in predictions[0]:
    print(movie_id_to_movie_title[movie_id])

``` ==> Movies the user has watched: 10 Things I Hate About You (1999), American Beauty (1999), Bachelor, The (1999), Austin Powers: The Spy Who Shagged Me (1999), Arachnophobia (1990), Big Daddy (1999), Bone Collector, The (1999), Bug's Life, A (1998), Bowfinger (1999), Dead Calm (1989)

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1/Unknown 0s 300ms/step

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 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step


    
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==> Recommended movies for the above sequence: Creepshow (1982) Bringing Out the Dead (1999) Civil Action, A (1998) Doors, The (1991) Cruel Intentions (1999) Brokedown Palace (1999) Dead Calm (1989) Condorman (1981) Clan of the Cave Bear, The (1986) Clerks (1994)

/usr/local/lib/python3.11/dist-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches. You may need to use the .repeat() function when building your dataset. self._interrupted_warning()

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Sequential retrieval [GRU4Rec]

Introduction

Dataset

Product

Resources

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