Power recommendations and search using an IMDb knowledge graph – Part 2

This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations using the IMDb and Box Office Mojo Movies/TV/OTT licensable data package, which provides a wide range of entertainment metadata, including over 1 billion user ratings; credits for more than 11 million cast and crew members; 9 million movie, TV, and entertainment titles; and global box office reporting data from more than 60 countries. Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention.

In Part 1, we discussed the applications of GNNs, and how to transform and prepare our IMDb data for querying. In this post, we discuss the process of using Neptune to generate embeddings used to conduct our out-of-catalog search in Part 3 . We also go over Amazon Neptune ML, the machine learning (ML) feature of Neptune, and the code we use in our development process. In Part 3 , we walk through how to apply our knowledge graph embeddings to an out-of-catalog search use case.

Solution overview

Large connected datasets often contain valuable information that can be hard to extract using queries based on human intuition alone. ML techniques can help find hidden correlations in graphs with billions of relationships. These correlations can be helpful for recommending products, predicting credit worthiness, identifying fraud, and many other use cases.

Neptune ML makes it possible to build and train useful ML models on large graphs in hours instead of weeks. To accomplish this, Neptune ML uses GNN technology powered by Amazon SageMaker and the Deep Graph Library (DGL) (which is open-source). GNNs are an emerging field in artificial intelligence (for an example, see A Comprehensive Survey on Graph Neural Networks). For a hands-on tutorial about using GNNs with the DGL, see Learning graph neural networks with Deep Graph Library.

In this post, we show how to use Neptune in our pipeline to generate embeddings.

The following diagram depicts the overall flow of IMDb data from download to embedding generation.

We use the following AWS services to implement the solution:

In this post, we walk you through the following high-level steps:

1. Set up environment variables
2. Create an export job.
3. Create a data processing job.
4. Submit a training job.
5. Download embeddings.

Code for Neptune ML commands

We use the following commands as part of implementing this solution:

%%neptune_ml export start
%%neptune_ml export status
%neptune_ml training start
%neptune_ml training status

We use neptune_ml export to check the status or start a Neptune ML export process, and neptune_ml training to start and check the status of a Neptune ML model training job.

For more information about these and other commands, refer to Using Neptune workbench magics in your notebooks.

Prerequisites

To follow along with this post, you should have the following:

• An AWS account
• Familiarity with SageMaker, Amazon S3, and AWS CloudFormation
• Graph data loaded into the Neptune cluster (see Part 1 for more information)

Set up environment variables

Before we begin, you’ll need to set up your environment by setting the following variables: s3_bucket_uri and processed_folder. s3_bucket_uri is the name of the bucket used in Part 1 and processed_folder is the Amazon S3 location for the output from the export job .

# name of s3 bucket
s3_bucket_uri = "<s3-bucket-name>"

# the s3 location you want to store results
processed_folder = f"s3://{s3_bucket_uri}/experiments/neptune-export/"

Create an export job

In Part 1, we created a SageMaker notebook and export service to export our data from the Neptune DB cluster to Amazon S3 in the required format.

Now that our data is loaded and the export service is created, we need to create an export job start it. To do this, we use NeptuneExportApiUri and create parameters for the export job. In the following code, we use the variables expo and export_params. Set expo to your NeptuneExportApiUri value, which you can find on the Outputs tab of your CloudFormation stack. For export_params, we use the endpoint of your Neptune cluster and provide the value for outputS3path, which is the Amazon S3 location for the output from the export job.

expo = <NEPTUNE-EXPORT-URI>
export_params={
    "command": "export-pg",
    "params": { "endpoint": neptune_ml.get_host(),
                "profile": "neptune_ml",
                "cloneCluster": True
                  },
    "outputS3Path": processed_folder,
    "additionalParams": {
            "neptune_ml": {
             "version": "v2.0"
             }
      },
"jobSize": "medium"}

To submit the export job use the following command:

%%neptune_ml export start --export-url {expo} --export-iam --store-to export_results --wait-timeout 1000000                                                              
${export_params}

To check the status of the export job use the following command:

%neptune_ml export status --export-url {expo} --export-iam --job-id {export_results['jobId']} --store-to export_results

After your job is complete, set the processed_folder variable to provide the Amazon S3 location of the processed results:

export_results['processed_location']= processed_folder

Create a data processing job

Now that the export is done, we create a data processing job to prepare the data for the Neptune ML training process. This can be done a few different ways. For this step, you can change the job_name and modelType variables, but all other parameters must remain the same. The main portion of this code is the modelType parameter, which can either be heterogeneous graph models (heterogeneous) or knowledge graphs (kge).

The export job also includes training-data-configuration.json. Use this file to add or remove any nodes or edges that you don’t want to provide for training (for example, if you want to predict the link between two nodes, you can remove that link in this configuration file). For this blog post we use the original configuration file. For additional information, see Editing a training configuration file.

Create your data processing job with the following code:

job_name = neptune_ml.get_training_job_name("link-pred")
processing_params = f"""--config-file-name training-data-configuration.json 
--job-id {job_name}-DP 
--s3-input-uri {export_results['outputS3Uri']}  
--s3-processed-uri {export_results['processed_location']} 
--model-type kge 
--instance-type ml.m5.2xlarge
"""

%neptune_ml dataprocessing start --store-to processing_results {processing_params}

To check the status of the export job use the following command:

%neptune_ml dataprocessing status --job-id {processing_results['id']} --store-to processing_results

Submit a training job

After the processing job is complete, we can begin our training job, which is where we create our embeddings. We recommend an instance type of ml.m5.24xlarge, but you can change this to suit your computing needs. See the following code:

dp_id = processing_results['id']
training_job_name = dp_id + "training"
training_job_name = "".join(training_job_name.split("-")) training_params=f"--job-id train-{training_job_name}  
--data-processing-id {dp_id}  
--instance-type ml.m5.24xlarge  
--s3-output-uri s3://{str(s3_bucket_uri)}/training/{training_job_name}/" 

%neptune_ml training start --store-to training_results {training_params} 
print(training_results)

We print the training_results variable to get the ID for the training job. Use the following command to check the status of your job:

%neptune_ml training status --job-id {training_results['id']} --store-to training_status_results

Download embeddings

After your training job is complete, the last step is to download your raw embeddings. The following steps show you how to download embeddings created by using KGE (you can use the same process for RGCN).

In the following code, we use neptune_ml.get_mapping() and get_embeddings() to download the mapping file (mapping.info) and the raw embeddings file (entity.npy). Then we need to map the appropriate embeddings to their corresponding IDs.

neptune_ml.get_embeddings(training_status_results["id"])                                            
neptune_ml.get_mapping(training_status_results["id"])                                               
                                                                                        
f = open('/home/ec2-user/SageMaker/model-artifacts/'+ training_status_results["id"]+'/mapping.info',  "rb")                                                                                   
mapping = pickle.load(f)                                                                
                                                                                        
node2id = mapping['node2id']                                                            
localid2globalid = mapping['node2gid']                                                  
data = np.load('/home/ec2-user/SageMaker/model-artifacts/'+ training_status_results["id"]+'/embeddings/entity.npy')                                                                           
                                                                                          
embd_to_sum = mapping["node2id"]                                                        
full = len(list(embd_to_sum["movie"].keys()))                                                                                                                                    
ITEM_ID = []                                                                            
KEY = []                                                                                
VALUE = []                                                                              
for ii in tqdm(range(full)):                                                         
node_id = list(embd_to_sum["movie"].keys())[ii]
index = localid2globalid['movie'][node2id['movie'][node_id]]
embedding = data[index]
ITEM_ID += [node_id]*embedding.shape[0]
KEY += [i for i in range(embedding.shape[0])]
VALUE += list(embedding)
                                                                       
meta_df = pd.DataFrame({"ITEM_ID": ITEM_ID, "KEY": KEY, "VALUE":VALUE})
meta_df.to_csv('new_embeddings.csv')

To download RGCNs, follow the same process with a new training job name by processing the data with the modelType parameter set to heterogeneous, then training your model with the modelName parameter set to rgcn see here for more details. Once that is finished, call the get_mapping and get_embeddings functions to download your new mapping.info and entity.npy files. After you have the entity and mapping files, the process to create the CSV file is identical.

Finally, upload your embeddings to your desired Amazon S3 location:

s3_destination = "s3://"+s3_bucket_uri+"/embeddings/"+"new_embeddings.csv"

!aws s3 cp new_embeddings.csv {s3_destination}

Make sure you remember this S3 location, you will need to use it in Part 3.

Clean up

When you’re done using the solution, be sure to clean up any resources to avoid ongoing charges.

Conclusion

In this post, we discussed how to use Neptune ML to train GNN embeddings from IMDb data.

Some related applications of knowledge graph embeddings are concepts like out-of-catalog search, content recommendations, targeted advertising, predicting missing links, general search, and cohort analysis. Out of catalog search is the process of searching for content that you don’t own, and finding or recommending content that is in your catalog that is as close to what the user searched as possible. We dive deeper into out-of-catalog search in Part 3.

About the Authors

Matthew Rhodes is a Data Scientist I working in the Amazon ML Solutions Lab. He specializes in building Machine Learning pipelines that involve concepts such as Natural Language Processing and Computer Vision.

Divya Bhargavi is a Data Scientist and Media and Entertainment Vertical Lead at the Amazon ML Solutions Lab,  where she solves high-value business problems for AWS customers using Machine Learning. She works on image/video understanding, knowledge graph recommendation systems, predictive advertising use cases.

Gaurav Rele is a Data Scientist at the Amazon ML Solution Lab, where he works with AWS customers across different verticals to accelerate their use of machine learning and AWS Cloud services to solve their business challenges.

Karan Sindwani is a Data Scientist at Amazon ML Solutions Lab, where he builds and deploys deep learning models. He specializes in the area of computer vision. In his spare time, he enjoys hiking.

Soji Adeshina is an Applied Scientist at AWS where he develops graph neural network-based models for machine learning on graphs tasks with applications to fraud & abuse, knowledge graphs, recommender systems, and life sciences. In his spare time, he enjoys reading and cooking.

Vidya Sagar Ravipati is a Manager at the Amazon ML Solutions Lab, where he leverages his vast experience in large-scale distributed systems and his passion for machine learning to help AWS customers across different industry verticals accelerate their AI and cloud adoption.