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Product categorization with machine learning

If nothing happens, download the GitHub extension for Visual Studio and try again. The goal of this project is to create a product classifier that will be used in product management software. Categorizing products on a product-by-product basis for thousands of products can be time consuming and error prone and is problem for which machine learning can be brought to bear.

Labelled data from existing businesses that belong to a data pool provides an opportunity to develop a machine learning model that is capable of automatically categorizing a set of existing, unlabelled products and labelling new products as they arrive in real time through electronic invoices.

By eliminating the need for new and existing users to manually categorize thousands of products, the onboarding process for product management software can be significantly improved; leading to a dygo boss ft bander minha xuga mp3 user experience and improved sales and marketing efforts.

product categorization machine learning

The current version of this project includes notebooks and application code to build machine learning models for the product classification task. The project does not currently include the contents of the data folder and is missing the initial ETL and exploratory notebooks.

The missing folder and notebooks will be added once the source data has been sanitized. In the meantime feel free to reach out if you have any questions about this codebase - calvindlm gmail. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. A machine learning project for product categorization. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit….

product categorization machine learning

AutoCat: Automated Product Categorization Overview The goal of this project is to create a product classifier that will be used in product management software. Project Structure.

ML-Powered Product categorization for smart shopping options

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Product Classification using Machine Learning-Part I

I am going in a very abstract layer for the problem, but I think this problem might be a common one so posting it. So, what I am looking for is any ML algorithm or Data Science technique is there to create a relationship factor between different sets of goods. I guess, its used heavily in social media, but here I am talking w. Like suppose if we sell a bunch of products together in a store, so in collection there will be many products that are always bought together. So, accordingly there will be a relationship factor between different products i.

So, is there any approach where we can tackle this problem to get the factor between two products using any technique? Edit : Narrowing it down to a universe where all items are having same factors and their relationship factor to each other totally depends on their occurance together.

As Sean Owen said, it's a wide problem so I can't be generic on this one. I'll just give you an example on how you could tackle this:. Let say you sell some products on a website. If you have different products, you can represent each sale by a dimensional vector, where each component is the count of ordered product. We use the same technique with words, and it is called bag of words. If you stop here, you only can perform statistical counts on individual product sales.

So let's use matrix factorization. If you sold products to people, then you can concat all your sales in a matrix of dimension X where each row represent one sale and each column represent one product.

Using decomposition algorithm such as SVDyou can find out singular values in your data that will show you latent factors in your sales basically trends or connections between your products. I assume you are looking for a similarity measure between items. A quick and simple one is item-item cosine similarity. The similarity between two products is then. Matrix decomposition techniques can probably give better results, but they might require a bit more knowledge of linear algebra or finding some ready-made products.

Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 2 years, 7 months ago. Active 2 years, 7 months ago. Viewed times. That's a lot of territory, so I think you'd have to narrow down from there what you are asking.

product categorization machine learning

Active Oldest Votes. I'll just give you an example on how you could tackle this: Let say you sell some products on a website. Robin Robin 1, 4 4 silver badges 17 17 bronze badges.This is a reresubmission. Therefore I will only focus on the issues I raised in my previous reviews:. In particular, the embeddings have been trained on the complete dataset, which makes the comparison to baselines potentially unfair. Therefore, the authors were asked to check the experimental protocol in general.

The authors as far as I read the feedback argue that this is fine. More precisely they touch 1 upon training test split resp. But this was not my point. My point is that the embeddings used were learned on the complete dataset. This induced no matter how you split a dataset a feedback loop from any test set to the training set for any machine learning system that is using the trained embeddings.

In turn, the machine learning system has seen at least implicitly the training set. This is an unfair comparison when comparing to classifiers that are not using the embeddings but only the splitting of the dataset, since they would not have seen at least implicitly the training set. And this is were I am confused. Even distances among tokens are a feedback that provides extra knowledge. To be more precise, the word2vec model is capturing the manifold structure of the complete dataset.

Imagine to use a k nearest neighbour classifier with fixed metric instead of a CRF. Regarding reference [2], to be honest, I think they have the same potential problem of a feedback loop from test to training. It is stated that only the l2 regularisation coefficients of the CRF have been cross validated. Anyhow, maybe the "solution" is the following. We add a line saying "Please not that the embeddings were trained on the complete dataset.

The embeddings are the categories and are likely to provide useful feedback, akin to a semi-supervised learning learning setting. Anyhow, I leave this decision to the action editor. In my opinion, the cleanest set up would be any data split and then the whole training including embedding generation is done on the training data only whether cross-validated or just training test splits. Please bag my pardon, but I was not talking e.

On page 11, it is said, that there are not significant but it is not said, which test was used. Maybe also a McN test, but this is unclear. This manuscript was submitted as 'full paper' and should be reviewed along the usual dimensions for research contributions which include 1 originality, 2 significance of the results, and 3 quality of writing.

Search form. Create new account Request new password. Tracking : A new version of this paper is available. Responsible editor:. Submission type:. Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops.Skip to search form Skip to main content You are currently offline.

Some features of the site may not work correctly. We present a method for classifying products into a set of known categories by using supervised learning. That is, given a product with accompanying informational details such as name and descriptions, we group the product into a particular category with similar products, e. To do this, we analyze product catalog information from different distributors on Amazon.

View PDF. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Topics from this paper. Supervised learning Categories Stemming Description. Statistical classification E-commerce payment system Baseline configuration management Large.

Citations Publications citing this paper. References Publications referenced by this paper. Information Structuring and Product Classification H. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.Product categories are the structural backbone of every online shop, but it can be quite a nightmare for e-commerce managers to make sure that all products are assigned to the correct categories.

The set of available categories is typically large Amazon has listed overchanges constantly, and new products have to be added on a daily basis. Mistakes can be costly, because miscategorized products not only look confusing and unprofessional, they also cannot be sold when customers are not able to find them. To improve the process of product categorization, we looked into methods from machine learning.

Our goal was to develop a machine learning system that can predict which categories fit best to a given product, in order to make the whole process easier, faster and less error-prone. In this blog post, I am going to walk you through the problems we faced on the way and how we decided to solve them.

From the perspective of machine learning, there are some unique challenges to the problem of predicting product categories:. To recap our goal: We want to build a machine learning system that predicts a broad range of product categories from names, images or descriptions.

These categories are then mapped to store-specific categories through a separate machine learning model. Our main coding language to build this system is Python.

Defining a good class set for this problem is a bit of an art form. If the set is too small, you might miss some categories that are important for a specific store.

But if the set is too large, prediction accuracies will drop significantly. We tested both larger and smaller sets and ended up with a set of categories in our current version.

The set is composed of rather broad terms, typically consisting of just one or two words. Here are some examples:. When we started, we tried to predict as many categories as we could imagine to ensure that we have a good coverage. But it is important to realize that our customers are not an evenly distributed group across the entire category landscape, but are more like clustered islands of particular industries.

When we get customers from entirely new industries, we can still adapt the class set, so that our models can evolve together with the needs of our customers.

Now that we know what we want to predict, we can start looking at the variables we want to use for the predictions. When it comes to image classification, convolutional neural networks are undoubtedly the gold standard, mainly because of their ability to identify and combine low-level features lines, edges, colors to more and more abstract features squares, circles, objects, faces.

We find a similar mechanism in the visual cortex of the human brain, so this algorithm must be doing something right. With the main breakthrough of convolutional neural networks inresearchers have consistently improved their architecture in the context of the annual ImageNet competition, which is something like the olympics of computer vision. Remember when I told you that we are going to need a particularly large amount of data to train a robust model for all these product categories?

We are going to cheat a little here with an approach called transfer learning. Training a state-of-the-art convolutional neural network from scratch takes a lot of time, data and computational resources, so we are just going to take a neural network Inception v3 that has already been pre-trained on a large image dataset.

This model has already learned a lot about extracting and combining image features, but it does not yet know which categories we want to have predictions for in our particular use case.

Top 10 Applications of Machine Learning - Machine Learning Application Examples - Edureka

To bend the network to our needs, we simply cut off the final classification layer, add a new layer with units corresponding to our product categories, and then retrain only these weights with our own dataset, while all the other model weights are frozen. We used the library TensorFlow in Python to implement this approach and ran model training on the Google Cloud ML Engine with modified code from this example. The code we wrote on top of that mainly deals with downloading images from urls, converting them to jpeg, rescaling them, sorting them into subfolders for each category, removing duplicates or invalid files, and uploading bottlenecks and trained models to Google Storage.Multi Label classification of products into the most relevant categories based on textual information present within same.

If you consider the English alphabet, the letters M and L are consecutive and moving from one letter to the other seems like a piece of cake. However, that may not be the case with Machine Learning ML. Our knowledge in the latter may be a receding mirage in an ever-expanding desert.

One thing that is often reiterated is the importance of understanding your problem statement to the very core. The problem at hand is to classify products to their most appropriate category or bucket. Indiamartan online marketplace that always requires similar products read here about products on Indiamart on its platform are listed under one head which should be representative of their characteristics and any new products should be assigned to the most appropriate head. For instance brown leather safety gloves should be listed under Leather Safety Gloves micro category and Safety Gloves macro category.

These macro and micro categories are listed under a Product Group. We would never want the user to visit our page for leather gloves and find woolen ones. We are only trying to ensure this correctness of product mapping.

This problem required classification at two levels — identifying both the macro and the micro categories. For us, each micro category is related to some macro category. In order to understand how we define macro and micro categories, let us take the example of a mobile phone.

A Product Group is like an umbrella for related macro categories. Our current architecture correctly recognizes this broader product umbrella so our main focus here would be the identification of macro and micro categories within a Product Group.

Thanks, Vikram Varshney for helping me sail through this. In our waterfall approach, there are two separate models-one for macro categories and the other for micro categories; both are invoked sequentially.

We have tried to descend the tree by predicting results at two steps: firstly, predict a macro category and then a micro category only within the predicted macro category. This is to say that for a product,once you get to know that it is an air conditioner macro category then we limit the prediction of micro categories split air conditionerwindow air conditioner ,cassette air conditioner etc to those within that macro category only.

So, I will not be looking for results under any other electrical appliances. We used FastText to train our model. F astText is an open source library from Facebook for efficient learning of word representations and sentence classification. In case you are new to FastText and face issues running it, please refer to this video tutorial. A supervised text-based classifier which was trained using previously labeled products was what we used.

The training files had products with each having appropriate labels for macro and micro category in separate files. Training File for Macro Category:. Training File for Micro Category:.

Training Command. The machine was trained on thousands of products against each macro and micro category label so that it builds a relation.

The next time we provide it with textual information for a product, it should able to decipher the macro and micro category for the same.This application claims benefit of priority to U. Provisional Application Ser. This application is related to U. Herein referred to as ' The present invention relates to data categorization. The invention is related more specifically to automatic product categorization.

The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

It is becoming increasingly common for shoppers to search for the particular product in which they are interested using electronic search mechanisms, such as Internet-based search engines. The complex systems used by such electronic search mechanisms to process incoming product data from multiple merchants, and deliver that product data in the form of search results to millions of customers, must ensure that customers receive the best information available.

Once obtained, the information must be categorized in order to, among other things, determine how much merchants associated with the product offerings are charged for inclusion of the product offerings in the corresponding search mechanism. Merchants are often charged a certain amount of money by the search engine owner every time a product of the merchant is selected by a user of the search mechanism—a cost-per-click CPC charge.

One approach to categorizing product offerings is the manual categorization approach. In the manual categorization approach, a human operator assigns product offerings to product categories. A problem with the manual categorization approach is that it is time and resource consuming, since a human operator must assign each product offering to a product category. Therefore, based on the foregoing, it is clearly desirable to provide a mechanism for automatic product categorization.

Techniques are provided for automatic product categorization. In one aspect, the categorization of product offerings is based, in part on one or more values that are separate and distinct from the actual text of the product offering. The existence of a particular field as opposed to the value of the field may also indicate particular product categories. In another aspect, a first categorization of a product offering is performed and, if the product category chosen is in a set of co-refinable product categories, then a second categorization is performed among the set of co-refinable product categories.

The set of co-refinable product categories may be determined in any appropriate manner, including by computing a confusion matrix based on differences between the results of a manual categorization and the results of an automatic categorization of a set of product offerings.

In another aspect, product offerings are categorized based at least in part on the cost of categorizing offerings into a particular product category. The cost of categorizing a product offering into a particular product category may be based on any appropriate equation, may be a penalty cost, and may be based on the revenue potentially lost if the chosen product category is the incorrect one. The product category is chosen for a product offering in part based on the cost associate with categorizing the product offering into the product category.

In another aspect, after products are categorized, further categorization processing is performed if the cost for categorizing the product is beyond a predefined limit. The same cost equations as used above may also be used here. The further processing may include flagging the product offering for manual review or performing a more computationally expensive categorization process on the product offering.