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Double-click SA202000CP1.exe to extract Sage 50 installation files.When the launch window, opens click Install Sage 50. With more than 230,000 unique catalog items spanning multiple brands and several product classes—including fashion, beauty, jewelry, and home—and more than 120,000 unique daily sessions, Ounass collects a wealth of browsing data.Modifying the chart of accounts When you created your company data file, Sage 50 automatically created a chart of accounts based on the business type selected in the Create a New Company wizard.While many of these accounts will exactly match your business needs, others may not.Insert the Sage 50 Accounting disc into your computer. Scouring the globe for leading trends, Ounass’s expert team reports on the latest fashion updates, coveted insider information, and exclusive interviews for customers to read and shop. However, there may be occasions when you need to manually install an update, for example if no updates are found.Based in Dubai, Ounass is the Middle East’s leading ecommerce platform for luxury goods. Follow the on-screen prompts. The easiest way to install any updates is to use the check for updates option in your Sage 50cloud Accounts software: On the menu bar click Help, then click Check for updates.
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The easiest way to install any updates is to use the check for updates option in your Sage 50cloud Accounts software: On the menu bar click Help, then click Check for updates. Running this recommender system in A/B tests, Ounass saw an average revenue uplift of 849% with respect to recommendations serving Ounass’s most popular items.Check for updates. We dive into defining the components that make up this architecture and the tools used to operate it. Accounting on the computer that will store your Sage 50 Accounting company data files.In this post, we (a joint team of an Ounass data scientist and an AWS AI/ML Solutions Architect), discuss how to build a scalable architecture using Amazon SageMaker that continuously deploys a Word2vec-Nearest Neighbors (Word2vec-NN) item-based recommender system using clickstream data.
Such item-based recommendations were previously generated using a rule-based recommender system that was tedious to maintain and handcrafted rules that were difficult to tune.Ounass needs an efficient, robust, scalable, and maintainable solution that adapts to the ever-changing customer preferences. As visitors browse the product details pages (PDPs) of Ounass Web, we want to serve them relevant product recommendations without requiring them to log in. For instance, the similarity between the embeddings of “cat” and “dog” would be greater than that between the embeddings of “cat” and “car” because the first pair of words are more likely to appear in similar contexts.Join us at one of our upcoming complimentary webinars where we will address the changes made to payroll: Update to Sage 50 2021.1: Mastering the Payroll Changes.At the time of writing, Ounass is accessible through the following platforms: iOS, Android, and web. These embeddings are learned so that they encode a degree of semantic similarity between words. Every word in the corpus used for training the model is mapped to a unique array of numbers known as a word vector or a word embedding. However, there may be occasions when you need to manually install an update, for example if no updates are found.Word2vec is a natural language processing (NLP) technique that uses a deep learning (DL) model to learn vector representations of words from a corpus of text.
As shown in the following figure, this approach allows us to build a corpus of items in which similar items are more likely to appear in similar contexts.Similarly to the example sentence, which is likely to end with the suggested words, the shown bags are more likely to appear after the sequence of products in the shown session.The Word2vec algorithm is trained on this corpus to learn the embeddings of all corpus items, which we refer to as the vocabulary. In analogy to NLP, we treat a browsing session as a sentence and item codes as words in that sentence. The sequence of items browsed during a session is assumed to encode the user’s stylistic preferences.As we collect the data from millions of sessions, each session carries with it a unique context that we can use to learn vector representations of items. For instance, when a user visits our platform with the intent of purchasing a red dress, they would probably look for similar outfits that appeal to their taste. Compute item embeddings with Word2vecWhen a visitor starts a browsing session on Ounass, we assume that they do it with the intent of either purchasing or discovering a particular product or set of related products. To measure success, we use metrics such as conversion rate uplift and revenue uplift.
Items that appear with higher frequency in the training data are randomly down-sampled. sampling_threshold – The threshold for the occurrence of items. Epochs – The number of complete passes through the training data, set to 110. min_count –Items that appear less than min_count times in the training data are discarded. Furthermore, when a user identifier is allowed, session-level embeddings can also be rolled up to compute user-level embeddings.To compute the item embeddings, we trained the batch skip-gram variant of Word2vec on the items corpus with a batch size of 128 and selected the following hyperparameters:
The context window is the number of items surrounding the target item used for training. window_size – The size of the context window. negative_samples – The number of negative samples for the negative sample sharing strategy, which we set to 6. vector_dim – The dimension of the computed item embeddings, set to 50. In analogy to NLP, such items correspond to articles like “the.” They might appear frequently in the corpus without necessarily carrying any session-level contextual information. This is done in order to shrink down the contribution of items that appear frequently in the corpus of items.
Computes the user’s K nearest neighbors in the item embedding space Builds a user embedding by averaging the embeddings of input SKUs We use CRM to pull the sequence of SKUs browsed by an identified user for the last N sessions and send it as input to our system, which does the following: Serves the SKU code of the K neighboring items as recommendations Determines the item’s K nearest neighbors in the embedding space On the platform, when a user browses the PDP of an item, the item’s Stock Keeping Unit (SKU) code is sent to our system, which does the following:
In the next stage, we use the SageMaker BlazingText algorithm to train a Word2vec model on the saved corpus of items using a distributed cluster of multiple GPU instances. The extracted data that contains the corpus of items is then saved in an Amazon Simple Storage Service (Amazon S3) bucket after transforming it into a format that can be consumed by the training algorithm. In the first stage, we extract the relevant session history by querying our unstructured data lake using Amazon Athena. The following diagram shows the overall end-to-end architecture, from training to inference, that we used to develop the scalable recommender that gets constantly refreshed.The workflow that we built using AWS Step Functions is divided into three main stages: To achieve this, we use the AWS Step Functions Data Science SDK.
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The experiment was run from May 22, 2021, to June 30, 2021. Test the solutionTo assess the utility of the solution, we ran an A/B test consisting of a series of email communications containing item recommendations. We use Amazon EventBridge to trigger the Step Functions workflow every 3 days to retrain the model with new data and deploy a new version of the model. When an inference request comes in, we generate the embedding for the SKU, calculate top K nearest items, and return the list back to the user in the response.We use an Amazon API Gateway layer in conjunction with an AWS Lambda function to handle the HTTPS requests from the front-end application to the SageMaker inference endpoint.This workflow is automatically triggered periodically to keep the model up to date with the new data and new items. Because the built-in BlazingText Word2vec deployment serves the embedding representation of an item by default, we built a custom inference Docker container that combines the nearest neighbors (NN) technique with the Word2vec model. In the last stage, we configure and deploy a SageMaker endpoint serving the Word2vec-NN recommendations.
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