{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Recommenders contributors.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Neural Collaborative Filtering on MovieLens dataset.\n",
"\n",
"Neural Collaborative Filtering (NCF) is a well known recommendation algorithm that generalizes the matrix factorization problem with multi-layer perceptron. \n",
"\n",
"This notebook provides an example of how to utilize and evaluate NCF implementation in the `recommenders`. We use a smaller dataset in this example to run NCF efficiently with GPU acceleration on a [Data Science Virtual Machine](https://azure.microsoft.com/en-gb/services/virtual-machines/data-science-virtual-machines/)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System version: 3.11.15 (main, Mar 11 2026, 17:20:07) [GCC 14.3.0]\n",
"Pandas version: 2.3.3\n",
"PyTorch version: 2.11.0+cu130\n"
]
}
],
"source": [
"import sys\n",
"import pandas as pd\n",
"import torch\n",
"\n",
"from recommenders.utils.timer import Timer\n",
"from recommenders.models.ncf.ncf_singlenode import NCF\n",
"from recommenders.models.ncf.dataset import Dataset as NCFDataset\n",
"from recommenders.datasets import movielens\n",
"from recommenders.datasets.python_splitters import python_chrono_split\n",
"from recommenders.evaluation.python_evaluation import (\n",
" map_at_k, ndcg_at_k, precision_at_k, recall_at_k\n",
")\n",
"from recommenders.utils.notebook_utils import store_metadata\n",
"\n",
"print(\"System version: {}\".format(sys.version))\n",
"print(\"Pandas version: {}\".format(pd.__version__))\n",
"print(\"PyTorch version: {}\".format(torch.__version__))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the default parameters."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"# top k items to recommend\n",
"TOP_K = 10\n",
"\n",
"# Select MovieLens data size: 100k, 1m, 10m, or 20m\n",
"MOVIELENS_DATA_SIZE = '100k'\n",
"\n",
"# Model parameters\n",
"EPOCHS = 50\n",
"BATCH_SIZE = 256\n",
"\n",
"SEED = 42"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Download the MovieLens dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:recommenders.datasets.download_utils:Downloading http://files.grouplens.org/datasets/movielens/ml-100k.zip\n",
"100%|██████████| 4.81k/4.81k [00:00<00:00, 5.10kKB/s]\n"
]
}
],
"source": [
"df = movielens.load_pandas_df(\n",
" size=MOVIELENS_DATA_SIZE,\n",
" header=[\"userID\", \"itemID\", \"rating\", \"timestamp\"]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Split the data using the Spark chronological splitter provided in utilities"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"train, test = python_chrono_split(df, 0.75)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filter out any users or items in the test set that do not appear in the training set."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"test = test[test[\"userID\"].isin(train[\"userID\"].unique())]\n",
"test = test[test[\"itemID\"].isin(train[\"itemID\"].unique())]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write datasets to csv files."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"train_file = \"./train.csv\"\n",
"test_file = \"./test.csv\"\n",
"train.to_csv(train_file, index=False)\n",
"test.to_csv(test_file, index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate an NCF dataset object from the data subsets."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:recommenders.models.ncf.dataset:Indexing ./train.csv ...\n",
"INFO:recommenders.models.ncf.dataset:Indexing ./test.csv ...\n",
"INFO:recommenders.models.ncf.dataset:Creating full leave-one-out test file ./test_full.csv ...\n",
" 0%| | 0/943 [00:00