Sold by: MphasisÂ
Deployed on AWS
This solution uses collaborative filtering to recommend top items for users based on their buying patterns.
Overview
Product Recommender finds similarities between users and items simultaneously to provide targeted recommendations. It analyzes information on users' buying patterns and recommends products to each user based on similarity with other users. Knowing the products which have high chance of being in customers' wish-list can be advantageous for an e-commece website or any retail store.
Highlights
- Anticipate product recommendation for targeted marketing, increasing cross-sell and for making promotions more effective.
- Helps users discover new interests based on purchase pattern similarity with other users. Can be applied in movies & songs websites, retail stores and e-commerce websites.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Bug fixes and performance enhancements
Additional details
Inputs
- Summary
A csv file with details around historical order and SKU details for the items purchased by individual customers.
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Product-Recommender-for-Retail/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Product-Recommender-for-Retail/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 |
|---|---|---|---|
ORDERID | ORDERID is the systematically assigned sequential code which is unique to each invoice
SKUNUMBER - Stock Keeping Unit ID
SKU DESCRIPTION - Description of item in string format. The name of item can contain brand and color name e.g. HOMEMADE JAM SCENTED CANDLES, JAM JAR WITH GREEN LID etc.
CUSTOMERID - Id associated with the customer | Type: FreeText
Limitations: More than one items may have same stock keeping unit id, but no item can have more than one stock keeping unit id | Yes |
SKUNUMBER | ORDERID is the systematically assigned sequential code which is unique to each invoice
SKUNUMBER - Stock Keeping Unit ID
SKU DESCRIPTION - Description of item in string format. The name of item can contain brand and color name e.g. HOMEMADE JAM SCENTED CANDLES, JAM JAR WITH GREEN LID etc.
CUSTOMERID - Id associated with the customer | Type: FreeText
Limitations: More than one items may have same stock keeping unit id, but no item can have more than one stock keeping unit id | Yes |
SKU DESCRIPTION | ORDERID is the systematically assigned sequential code which is unique to each invoice
SKUNUMBER - Stock Keeping Unit ID
SKU DESCRIPTION - Description of item in string format. The name of item can contain brand and color name e.g. HOMEMADE JAM SCENTED CANDLES, JAM JAR WITH GREEN LID etc.
CUSTOMERID - Id associated with the customer | Type: FreeText
Limitations: More than one items may have same stock keeping unit id, but no item can have more than one stock keeping unit id | Yes |
CUSTOMERID | ORDERID is the systematically assigned sequential code which is unique to each invoice
SKUNUMBER - Stock Keeping Unit ID
SKU DESCRIPTION - Description of item in string format. The name of item can contain brand and color name e.g. HOMEMADE JAM SCENTED CANDLES, JAM JAR WITH GREEN LID etc.
CUSTOMERID - Id associated with the customer | Type: FreeText
Limitations: More than one items may have same stock keeping unit id, but no item can have more than one stock keeping unit id | Yes |
Resources
Vendor resources
Support
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Customer reviews
model issue
Can not get support really strange issue
Reviewed on Oct 11, 2022
Review from a verified AWS customer
Computing the msd similarity matrix...
/usr/local/lib/python3.6/dist-packages/werkzeug/filesystem.py:60: BrokenFilesystemWarning: Detected a misconfigured UNIX filesystem: Will use UTF-8 as filesystem encoding instead of 'ascii'
BrokenFilesystemWarning,
Debugging middleware caught exception in streamed response at a point where response headers were already sent.
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/werkzeug/wsgi.py", line 506, in next
return self._next()
File "/usr/local/lib/python3.6/dist-packages/werkzeug/wrappers/base_response.py", line 45, in _iter_encoded
for item in iterable:
TypeError: 'MemoryError' object is not iterable
169.254.255.130 - - [04/Oct/2022 11:27:27] "POST /invocations HTTP/1.1" 200 -
Computing the msd similarity matrix...
/usr/local/lib/python3.6/dist-packages/werkzeug/filesystem.py:60: BrokenFilesystemWarning: Detected a misconfigured UNIX filesystem: Will use UTF-8 as filesystem encoding instead of 'ascii'
BrokenFilesystemWarning,
Debugging middleware caught exception in streamed response at a point where response headers were already sent.
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/werkzeug/wsgi.py", line 506, in next
return self._next()
File "/usr/local/lib/python3.6/dist-packages/werkzeug/wrappers/base_response.py", line 45, in _iter_encoded
for item in iterable:
TypeError: 'MemoryError' object is not iterable