{"id":3316,"date":"2017-08-30T10:01:12","date_gmt":"2017-08-30T10:01:12","guid":{"rendered":"https:\/\/www.openbusinesscouncil.org\/?p=3316"},"modified":"2021-05-07T08:52:54","modified_gmt":"2021-05-07T08:52:54","slug":"love-sales-launches-ai-system-changes-face-retail-merchandising","status":"publish","type":"post","link":"https:\/\/www.footballthink.com\/love-sales-launches-ai-system-changes-face-retail-merchandising\/","title":{"rendered":"Love the Sales launches AI system that changes the face of retail merchandising"},"content":{"rendered":"
\"Love
Love the Sales launches AI system that changes the face of retail merchandising<\/figcaption><\/figure>\n

<\/h1>\n

Get more bytes for your buck. How to squeeze the most out of your training data.<\/h1>\n

In many cases, the acquisition of well-labelled training data is a huge hurdle for developing accurate prediction systems with supervised learning. At Love the Sales, we had the requirement to apply classification to the textual metadata of 2 million products (mostly fashion and homewares) into 1000+ different categories – represented in a hierarchy.<\/p>\n

In order to achieve this, we have architected a hierarchical tree of chained 2-class linear (Positive vs Negative) Support Vector Machines (LibSVM), each responsible for binary document classification of each hierarchical class.<\/p>\n

Data structures<\/h2>\n

A key learning, is that the way in which these SVM\u2019s are structured can actually have a significant impact on how much training data has to be applied, for example, a naive approach would have been as follows:<\/p>\n

\n

This approach requires that for every additional sub-category, two new SVM\u2019s be trained – for example, the addition of a new class for \u2018Swimwear\u2019 would require an additional SVM under Men’s and Women’s – not to mention the potential complexity of adding a \u2018Unisex\u2019 class at the top level. Overall, deep hierarchical structures can be too rigid to work with.<\/p>\n

We were able to avoid a great deal of labelling & training work, by flattening our data structures into many sub-trees like so:<\/p>\n

\n


By decoupling our classification structure from the final hierarchy, it is possible to generate the final classification by traversing the SVM hierarchy with each document, and interrogating the results with simple set-based logic such as:<\/p>\n

Mens Slim-fit jeans = (Mens and Jeans and Slim Fit) and not Womens<\/p>\n

This approach vastly reduces the number of SVM\u2019s required to classify documents, as the resultant sets can be intersected to represent the final classification.<\/p>\n

 <\/p>\n

\n

It should also now be evident that adding new classes opens up an exponentially increasing number of final categories. For example – adding a top-level \u2018Children’s\u2019 class – would immediately allow the creation of an entire dimension of new Children\u2019s categories (childrens jeans, shirts, underwear, etc), with minimal additional training data (Only one additional SVM):<\/p>\n

Data reuse<\/h2>\n

Because of the structure we chose, one key insight that we were able to leverage, was that of re-using training data, via linked data relationships. Linking data enabled us to re-use our training data by an overall factor of 9x – thus massively reducing the cost and increasing the accuracy of predictions.<\/p>\n

For each individual class, we obviously want as many training-data examples as possible, covering both possible outcomes. Even though we built some excellent internal tooling to help with manually labelling training data in bulk – labelling thousands of examples of each kind of product can be laborious, costly and error-prone. We determined the best way to circumvent these issues was to attempt to reuse as much training data as we could, across classes.<\/p>\n

For example, given some basic domain knowledge of the categories – we know for certain that \u2018Washing machines\u2019 can never be \u2018Carpet cleaners\u2019<\/p>\n

\n

By adding the ability to link \u2018Exclude data\u2019, we can heavily bolster the amount \u2018Negative\u2019 training examples for the \u2018Washing machines\u2019 SVM by adding to it the \u2018Positive\u2019 training data from \u2018Carpet cleaners\u2019 SVM. Put more simply, given that we know \u201cCarpet cleaners can never be washing machines\u201d – we may as well re-use that data.<\/p>\n

This approach has a nice uptick, in that whenever the need arises to add some additional training data to improve the \u2018Carpet Cleaners\u2019 SVM – it inadvertently improves the \u2018Washing machines\u2019 class, via linked negative data.<\/p>\n

Finally, another chance for reuse, that is apparent when considering a hierarchy, is that the positive training data for any child nodes, is also always positive training data for it\u2019s parent. For example: \u2018Jeans\u2019 are always \u2018Clothing\u2019.<\/p>\n

\n

This means that for every positive example of training data added to the \u2018Jeans\u2019 SVM – an additional positive example is also added to the \u2018Clothing\u2019 SVM via the link.<\/p>\n

Adding links is far more efficient than manually labelling 1000\u2019s of examples.<\/p>\n

\n

When it comes to hierarchical classification systems, decoupling the classification component from the resulting hierarchy, flattening the data structure and enabling the reuse or training data will all be beneficial in gaining as much efficiency as possible. The approaches outlined above have not only helped reduce the amount of training data we needed to label, it has also given us greater flexibility overall.<\/p>\n","protected":false},"excerpt":{"rendered":"

Get more bytes for your buck. How to squeeze the most out of your training data. In many cases, the acquisition of well-labelled training data is a huge hurdle for developing accurate prediction systems with supervised learning. At Love the Sales, we had the requirement to apply classification to the textual metadata of 2 million […]<\/p>\n","protected":false},"author":7,"featured_media":3317,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_mo_disable_npp":""},"categories":[25,1],"tags":[],"acf":[],"yoast_head":"\nLove the Sales launches AI system that changes the face of retail merchandising - OpenBusinessCouncil Directory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.footballthink.com\/love-sales-launches-ai-system-changes-face-retail-merchandising\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Love the Sales launches AI system that changes the face of retail merchandising\" \/>\n<meta property=\"og:description\" content=\"Get more bytes for your buck. How to squeeze the most out of your training data. In many cases, the acquisition of well-labelled training data is a huge hurdle for developing accurate prediction systems with supervised learning. 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