This document summarizes major improvements and fixes introduced in the Accelerate Product Recommendation Optimization package release version.

Version	1.0.2
Release Date	Jul 18, 2023

New Features and Improvements

Description	ID

Description	ID
Python engine is now automatically deployed to the partition (if not already present) together with the accelerator.	PFPCS-6938

Fixed Issues

Bug Description	ID

Bug Description	ID
Product Recommendation step fails when Pricing Date is used for Basket ID but this field is not unique for each product-customer pair. To fix this issue, this corner case was implemented: transaction date is used for Basket ID and a customer orders the same product multiple times on a single date.	PFPCS-6808
The Product Recommendations step fails with an error "Input X contains NaN. AgglomerativeClustering does not accept missing values encoded as NaN natively..." This happens when the selected data only contains a single customer and model generated segments are selected. To fix this issue, recommendations for a single customer are now produced.	PFPCS-6811
When using model generated customer segments, you can get an error "cannot extract more clusters than samples".	PFPCS-6824
Product Recommendation's SegmentMapTable DMT fields are not classified (dimension vs. measure) and thus cannot be used by the Data Table input.	PFPCS-6831
The Product Recommendation step fails with an error "duplicate key value".	PFPCS-6862