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Release Summary - Aug 10, 2023

The following key features and improvements, along with bug fixes, have been released in Algonomy products in the release version 23.18 during July 28 –Aug 10, 2023.

Enterprise Dashboard

Enhanced Social Proof Reporting with Visualizations

We have enriched the Social Proof reporting experience by introducing graphical visualizations for key metrics. As a Digital Optimization Manager, you can now effortlessly view and analyze important metrics without the need for dynamic experiences in your product portfolio.

Visualizations have been added for key metrics including Visits, Views, ATC Rate, Sales, Orders, View Conversion Rate, View Revenue Per Visit, View Converted Visits, and View-based ATC Visits. These line graph visualizations provide insights into daily trends for the specified date range and channels.

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Jira: ENG-26475

Enhanced Placement Selection for Advanced Merchandising Rules

We have introduced improvements to the placement selection process in the Advanced Merchandising (AM) rules. As a digital merchandiser, you can now conveniently select placements for your rules, enhancing the configuration process. The new enhancement includes a search field that enables you to filter through the list of placements. As you type, the list dynamically updates to match your input, streamlining the process of finding specific placements. For example, typing "item" will display only placements with "item" in their names.

Furthermore, we've upgraded the placement selection to support multi-select functionality. This enhancement allows you to efficiently pick multiple placements simultaneously, saving you valuable time and effort.


Jira: ENG-26231

Default Start Date Enhancement in Advanced Merchandising Rules

We have introduced an enhancement to the Advanced Merchandising (AM) rules configuration. To streamline the rule creation process, the default start date has been set to today's date. When creating a new AM rule, the start date will now be automatically populated with the current date.

This enhancement simplifies the workflow by eliminating the need for manual input of the start date, resulting in quicker and more efficient rule creation.

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Jira: ENG-26232

Uniform Preview Image Heights in Advanced Merchandising Rule Context

To create a uniform and visually appealing experience, we have applied a solution that enforces a maximum height of 170px for all preview images. This adjustment guarantees a standardized presentation of preview products, regardless of the varying image dimensions found across different sites. This enhancement contributes to a seamless and harmonized visual representation of your merchandising content.

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Jira: ENG-26130

Refinements to the New Advanced Merchandising User Interface

We've implemented adjustments to the new AM UI in this release, providing digital merchandisers with an improved experience for managing Advanced Merchandising rules. Now, each Recommendation Group is organized within its own tab, making it convenient for merchandisers to view and configure recommended products alongside seed products.

We've also incorporated intuitive functionalities, such as reordering tabs through drag-and-drop, creating new Recommendation Groups, and a dedicated 'All products' tab for consolidated viewing. Merchandisers can now seamlessly manage and fine-tune their Advanced Merchandising rules, enhancing their overall workflow.

Jira: ENG-26230

Enhanced Do Not Recommend Rule Functionality for Image URL Filtering

We have introduced an enhanced feature to the Do Not Recommend rules, catering to the specific needs of our users. As a digital optimization manager, you can now create more targeted rules to prevent the recommendation of products based on their image URLs.

The newly added 'Image URL' tab within the Do Not Recommend rules provides you with a powerful tool to refine your recommendations. By selecting the 'Products where the image URL contains' option and specifying the desired matching text, you can effectively exclude products with specific image URLs from being recommended.

Jira: ENG-26193

Enhanced Recommendation Filtering Based on User's History

This release introduces new filtering capabilities within the Do Not Recommend rules, enabling you to strategically exclude products that share attribute values with items in your users' view or purchase history.

Under the User tab of the Do Not Recommend rules, we have integrated two new options:

'Filter products with a matching attribute value as products the user has already viewed'

'Filter products with a matching attribute value as products the user has already purchased'

When enabled, these options allow you to specify an attribute name and a timeframe in days. Subsequently, products with the same attribute value as those in the user's view or purchase history within the specified timeframe will be excluded from recommendations. This feature ensures that users are presented with a diverse range of products for discovery, enhancing their shopping experience.

Jira: ENG-25859

UI Enhancement for Session Report - Top 1000 Users by Different IDs per Day

In this release, we've implemented a comprehensive UI enhancement for the Session Report, tailored to the needs of the Digital Optimization, Support, and Field teams. This new feature allows users to analyze the top 1000 users per day using different ID types: External User ID, RR User GUID, and External Session ID.

Each ID type has its own dedicated table, presented in separate tabs, making it effortless to navigate and understand the distinct user groups. The report provides essential data fields including Event Date, Site ID, Site Name, the relevant User ID or Session ID (depending on the report), and crucial metrics such as Views, Clicks, Orders, and Sales. It's important to note that separate tables are employed for each ID type to accommodate scenarios where a single external session ID may be associated with multiple user GUIDs, and vice versa.


Jira: ENG-25646

Enterprise Dashboard / Find

Fixed Search Test Drive issues

We have resolved the issues with the Search Test Drive feature. The primary concern of not saving to production, which was only affecting integration, has been addressed.

Additionally, we have streamlined the user interface by eliminating unnecessary fields such as placements and languages that were causing confusion. Decimal value discrepancies have been rectified, enabling the successful addition of boosts. Notably, we've filtered out Engage placements from the dropdown, enhancing the user experience.

JIRA: ENG-26312


Enhanced Strategy for MultiSolrRecentSearchToPurchase Recommendations

We have enhanced the MultiSolrRecentSearchToPurchase recommendation strategy to ensure more relevant recommendations for shoppers. Previously, the strategy utilized wildcard (*) search terms as seeds, potentially leading to irrelevant product recommendations. As a solution, we have implemented the following improvements:

Queries with less than 2 characters are no longer utilized as seeds for MultiSolrRecentSearchToPurchase and RecentSearchToPurchase strategies.

Recommendations are now based on valid search terms, eliminating the occurrence of wildcard (*) as a seed for product recommendations.

This enhancement ensures that shoppers receive more accurate and contextually relevant recommendations based on their search history.

Jira: ENG-25962

Data Engineering

Enhanced S3 Import Job for Multi-region Support

We've extended the capabilities of our data import job, addressing a crucial need in our analytics migration efforts. The job can now handle data from multiple regions, including EU and APAC, by efficiently moving data to designated AWS S3 buckets based on the specific region. This enhancement ensures a seamless transition of data, enabling us to cater to the requirements of different regions.

Jira: ENG-25933

Other Feature Enhancements

The following feature enhancements and upgrades have been made in the release version 23.18 during July 28 –Aug 10, 2023.

Jira #



General Availability


Enterprise Dashboard:

Attribute-Based Filtering for User View and Purchase History

We have introduced the ability to filter out products with matching attribute values based on a user's view and purchase history. As a digital merchandiser, you can now enhance recommendation strategies by preventing the recommendation of products that share the same attribute value as items the user has previously viewed or purchased.

The new API functionality allows you to specify the attribute name and a time frame within which the filtering should apply. If no time limit is desired, a value of -1 can be used.

This helps to ensure that product recommendations are more personalized and aligned with user preferences.



Enterprise Dashboard:

Multi-Select for Guided Selling Questions

We have introduced the ability to make certain Guided Selling questions multi-select, enhancing the customization options for generating relevant recommendations. When configuring a Guided Selling screen, a new option "Allow multiple answers" has been added, specifically applicable to the Product Attribute question type. Once enabled, end-users can select multiple answers, effectively using checkboxes instead of radio buttons.

Note: This option is disabled and grayed out when the question type is not Product Attribute.



Enterprise Dashboard / Find:

Fixed Search Test Drive Issues

We have resolved the issues with the Search Test Drive feature. The primary concern of not saving to production, which was only affecting integration, has been addressed.

Additionally, we have streamlined the user interface by eliminating unnecessary fields such as placements and languages that were causing confusion. Decimal value discrepancies have been rectified, enabling the successful addition of boosts. Notably, we've filtered out Engage placements from the dropdown, enhancing the user experience.




ChatGPT Integration in Catalog Enrichment

In this release, we have introduced ChatGPT integration for Catalog Enrichment. This feature empowers optimization managers to enhance product attributes, aiding shoppers in finding the right products more effectively. By enabling ChatGPT through site configuration, users can leverage enriched product descriptions while maintaining cost efficiency. This integration streamlines the enrichment process by avoiding redundant submissions of product descriptions, and it allows for manual full catalog scans twice within a 90-day period. Additionally, attribute additions and manual scans are supported, providing a comprehensive solution for optimizing catalog enrichment efforts.




Composite Outfit - fallback to product to category CP

Optimization managers can now provide outfits based on the probability that a product would be purchased or viewed with a category of products to increase the outfits presented.




Composite Outfit - Update model approach

We have revamped the Composite Outfit generation approach to improve outfit relevance. Outfits are now curated based on both "purchased together" and "viewed together" scores, favoring purchased together suggestions by a factor of 5. Additionally, outfits are intelligently sorted by category considerations, enhancing customer engagement. Duplicate products, except for the seed product, have been removed from outfits, streamlining the shopping experience. This update ensures more appealing and tailored outfit recommendations for users.




Composite Outfit - Design API to display outfits

In this release, we have designed a comprehensive API for displaying outfits, catering to different scenarios. Given a product seed, the API returns multiple outfits, ranked by their success score, including product IDs, part IDs, and style names. Outfits are de-duplicated, ensuring a unique user experience, and optional product de-duplication prevents repetition. The API supports style, category, brand, or no seed inputs, tailoring outfit recommendations accordingly. Notably, no merchandising rules are required, and outfits are created based on merchandising specifications, akin to Advanced Merchandising.




Composite Outfit - Portal API to define and manage Styles

In this release, an essential enhancement has been introduced as part of the "Composite Outfit" feature. We have introduced a dedicated Portal API designed to define and manage styles for outfits. This addition empowers merchandisers by allowing them to create, customize, and organize styles with ease. Within this enhanced framework, merchandisers can establish styles, specify attributes, review and exclude products or outfits, and gain insights into their created styles. This streamlined process ensures a more efficient and intuitive approach to managing outfits, contributing to an improved overall merchandising experience.




Log4j Update: Marathon Component Enhancements

We have made necessary updates to the Marathon components in response to a log4j update. These updates have been implemented and tested in our QA environment. The changes have been successfully completed and verified. Everything is functioning as expected.




Create Recommend Rules with the image_url field

The optimization manager needed to create a do not recommend rule when the image URL is not a real image, to prevent it from being recommended.

Added an option to the ‘Do Not Recommend’ section to select Image URL and then identify the text that the image URL contains when it should not be recommended.


PLAT-3529, PLAT-3530, PLAT-3532, PLAT-3535

Platform > Streaming Catalog:

New enrichment components

We have introduced several new components, including enrichment calculation, enrichment subscription, enrichment ingestion, and enrichment view components in Enrichment Version 2. These components utilize the streaming catalog enrichment APIs for Enrichment Version 2.





Enhanced Snapshot View with Subscription Sections

We have introduced a significant improvement to our streaming-snapshot feature, allowing users to gain deeper insights. By utilizing the new query parameters "sections=subscriptions" and "subscriptionState," you can now view subscriptions associated with a specific snapshot. When using "sections=subscriptions," the response will display active subscriptions, offering valuable real-time data. This feature works seamlessly for creating, complete, and active snapshots, enhancing your snapshot analysis capabilities.





Enable republish dataset by site via a REST call

We have enhanced the dataset republishing process by introducing a new capability: republishing datasets by site through a REST call. This improvement addresses an issue where the enrichment ingest API encountered errors due to a missing parameter in the calculation service HTTP call. As a result, dataset synchronization during republishing was incomplete.

This enhancement ensures the successful republishing of datasets, facilitating seamless synchronization between various components.




e2e WOC feature with Enrichment V2

We have successfully integrated the end-to-end Wisdom of the Crowd (WOC) feature with Enrichment V2. This integration marks a significant transition of dataset management from calculation to the site.





Refactor Woc SparkJob  Enrichment API according to v2


We have successfully refactored the Woc SparkJob Enrichment API to align with the Enrichment V2 framework. This adjustment ensures compatibility and consistency with the updated version, enhancing the overall efficiency and functionality of the system.


Bug and Support Fixes

The following issues have been fixed in the release version 23.18 during July 28 –Aug 10, 2023.





General Availability



Enterprise Dashboard:

Placement Selection and Freezing Screen Issue

We have resolved an issue that prevented users from selecting placements and caused the screen to freeze while creating restriction rules. Previously, when attempting to select a placement during the creation of a Do Not Recommend Rule, the screen would become unresponsive and stuck in a loading state.



Enterprise Dashboard:

Variation Duplication Issue in Social Proof Campaigns

We have resolved an issue where duplicating a Social Proof campaign/experience was failing to copy the associated variations. This bug resulted in incomplete duplication, impacting the smooth creation of campaigns. We have addressed this by ensuring that when duplicating campaigns, the variations are now fully copied, mirroring the entire experience without publishing the variations.



Enterprise Dashboard:

Resolved Unexpected Error in Social Proof Item Page Template

We have fixed an issue that was causing unexpected errors in the item page template of Social Proof (SP). Users were encountering errors while interacting with the item page template. The issue has been fixed now.



Enterprise Dashboard / Find:

Incorrect Data Source in "Search Test Drive"

We have resolved an issue where the "Search Test Drive" feature in the QA environment was fetching data from the staging environment instead of the intended QA environment. This issue occurred due to an incorrect internal API call that directed to the wrong URL. We have fixed this issue by ensuring that the "Search Test Drive" functionality correctly fetches data from the QA environment, with the API call now correctly directed to




Resolved Issue with "Do Not Recommend"

Rule in Configurable Strategies

We have resolved an issue related to the "Do Not Recommend" rule functionality within Configurable Strategies. Previously, the restriction rule "Products Viewed" was not functioning correctly, causing recommendations to appear for items that users had viewed in the past X days. With the necessary fixes in place, the restriction rule now operates as intended, ensuring that recommendations align with user behavior.




API Publish Issue Resolution

We have successfully resolved the issue related to the API call for publishing on the search page. Previously, this API call exhibited a 500 error for certain clients, while it performed successfully for others. We have rectified the problem, ensuring that the publish API now functions as intended for all clients. The issue has been fully resolved and confirmed with a 200 OK status code in the API response.




Affinity sorting seems apply to only the default configuration

When selecting User Affinity sorting, the recs should be sorted based on the User Affinity Configuration that was selected. However, it appears as though sorting is always based on the default configuration.

It’s possible that this is an expected behavior. It all depends on what products are in the base model. If there aren’t products from those categories in the base model, then we wouldn’t expect to see them in the affinity version. The affinity results are just the same products as the base model, but they are re-sorted based on the affinity score. It sounds like this is working as expected.



Data Engineering:

Strategied Detailed report In QA throwing empty row with ‘0’ value

Records with strategy_id=-1 were filtered out from all the Site Analytics related reports, including the Strategies report, where the issue was observed. This issue was not visible in OnPrem as it was applying inner join for non-admin User. The issue has been fixed now.



Data Engineering: [Reports] Graph Fails to Refresh After Metric Changes

We have resolved an issue where the graph was not refreshing after changing the metrics in reports. The problem has been addressed, and the graph now updates seamlessly upon metric changes.


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