Sold by: Mphasis
Deployed on AWS
A quantum simulator based Recommender System for cross-sell recommendations of products.
Overview
This is a state of art quantum simulator-based solution that identifies the products that has a higher probability of purchase by a buyer based on the past purchase patterns. This solution helps businesses to achieve better cross-sell and improved customer life time value. This will also help businesses such as Retail, e-Commerce, etc. to plan their marketing strategy and product promotions based on historical purchase patterns.
Highlights
- The solution leverages the capability of quantum simulator to efficiently sift through vast amounts of historical purchase data to quickly identify patterns and provide product recommendations given the products in the user’s cart. This improves user experience and customer satisfaction. The solution uses Graph based recommendation system which helps to accurately recommend products.
- The solution can be used in e-Commerce, Retail, Over the top media service (OTT platform), Telecom and Banking.
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Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $40.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $40.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $40.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $40.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $40.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $40.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $40.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the Version 3.4
Additional details
Inputs
- Summary
The solution needs “.zip” file containing the following two files in CSV (UTF-8 encoded) format containing requisite fields:
- data.csv: Fields are as follows
- order_id: Purchase Order ID
- product_id: purchased product ID
- product_name: The name of the product for the corresponding ID
- user_input.txt: Fields are as follows:
- user_input: The product IDs for which a recommendation is required
- data.csv: Fields are as follows
- Limitations for input type
- Please make sure that the order of fields in data.csv is as specified above. The code can take data of 2000 products or 15000 rows which ever is less
- Input MIME type
- text/csv, application/json, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Cross-sell-Recommender/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Quantum-Simulator-Cross-sell-Recommender/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
order_id | Specifies the order ID for each purchase made by the customer | Type: Integer
Minimum: 0 | Yes |
product_id | Specifies the product ID corresponding to each order ID for the products purchased by the customer | Type: Integer
Minimum: 0 | Yes |
product_name | Specifies a unique name for each product ID purchased by the customer | Type: FreeText
Allowed values: SWEETHEART CAKESTAND 3 TIER, PLACE SETTING WHITE HEART, etc | Yes |
user_input | Specifies the product IDs for which the customer want a recommendation. If multiple product IDs are to be specified then they should be separated by commas | Type: Integer | Yes |
Resources
Vendor resources
Support
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