Online Grocery Recommendation Using Collaborative Filtering Github – The consumer retail industry is large, high turnover, small margins, but full of opportunity. Photo by Markus Spieske on Unsplash.
Recommender systems are becoming more popular – every analyst, data scientist and data engineer in retail and consumer-related businesses, whether in e-commerce or traditional offline businesses, will encounter these inevitable and important applications of Machine Learning. It is not an exaggeration to rate it as the highest impact ML product. E-commerce has benefited greatly from predicting machine learning to improve business operations and increase sales revenue. A recommendation system using an ML algorithm seems to be an integral part of any retailer, e-commerce retailer and merchant, not only because of its simplicity, but also because of its ability to unlock business value. hidden within a large amount of information. transaction data. It also goes beyond the intuition of traditional marketing and offers solutions based on fact, not just guesswork. His ability to produce products that typically end up in the long tail or introduce unexpected elements into a retail portfolio has caught the attention of C-Suite business executives and retail owners.
Online Grocery Recommendation Using Collaborative Filtering Github
In this article, I will use the TensorRec module in Python to provide a simple application in a retail business, a deep learning ML algorithm using Tensorflow and Keras backend, with a very simplified interface. I think it’s easy to use for beginners, but strong and solid. A prerequisite is knowledge of Python and Pandas, and the rest of the code should be easy to understand, all available in my Github repository.
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There are many recommendation algorithms, from simple association rule to slightly more complex K-Nearest Neighbor clustering, but are they good enough to solve real business with millions of historical retail data intertwined with hundreds of categories? , customer and business hierarchies? goals? A collaborative filtering prototype might work for a small mom and dad’s grocery store, but if you’re running multiple supermarket chains in a city, the complexity can scale to millions of transactions. Therefore, you need a better algorithm to handle the complexity and produce results that are relevant and meaningful to your business. In my opinion, hybrid deep learning algorithm works best for large and complex datasets. By combining Pandas, Numpy, and SKLearn, as well as the TensorRec package module, we can use the full capabilities of Python to produce robust, robust, and relevant article recommendations.
There are many good articles that provide an introduction to referral systems. A brief overview of Tensorrec is provided here by James Kirk. There is another good article that provides an example of using Tensorrec for movie recommendation using the MovieLens dataset. Much of the code on this page is based on the work of these two websites. Those interested in seeing the actual TensorRec page can also view the original Github page.
This system uses the explicit characteristics of elements to represent the interactions between them. For example, if a user has purchased an item (such as socks), the algorithm suggests a similar or related item (such as shoes).
This system is based on the idea of ”user who likes X also likes Y”, where the user’s historical transaction between these two entities is chained to build relationships between other users. If user X has purchased milk, user Y, who is similar to user X but has not yet purchased the item, will receive an offer to purchase it.
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The combination of the two methods that I will show in this article is just a hybrid of them, which fully handles both user identity and element identity.
This article uses an open dataset available on Kaggle. This data set is retail transactions from January 2011 to 2014, the full data set and description is available here. I’ve put the data in my Github repository for easy access here. This data set is not a perfect sample, but it is sufficient for the purpose of this demonstration. The goal here is to understand how to use a recommender system for sellers with multiple customers or systems with multiple users.
We use Pandas to fully extract the meaning of the data before transporting it to the TensorRec engine. A good opportunity to learn how to clean and manipulate data. I encourage you to work with the code in Jupyter Notebook or any other Python interface of your choice, all of which are available on my Github. I’ll just explain the basic concepts and leave most of the details to the code.
As mentioned above, a recommendation engine can use collaborative filtering, content-based filtering, or a hybrid of the two. Since we have sample data that perfectly (or almost perfectly) captures not only the interactions between users and objects, but also the characteristics of users, we can use the hybrid approach without any problems. transaction data (
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) serves as a joint function because it describes a user’s (or customer’s) transaction: what they bought and when, and the customer (
Features may be explicit or implicit, visible or hidden, or derived at face value or through some feature engineering. As a data scientist, feature engineering is an important part of data wrangling, necessary in almost all ML algorithms, but it is a recommender system. This sample data doesn’t always tell us everything we need, but as experienced data scientists (as I like to call ourselves ninjas), we need to know how to uncover hidden value. To achieve this, we do some unsupervised ML and transaction data wrangling to get new features.
The key to Tensorrec is understanding that there are three important components: user roles, object roles, and interactions. As shown in the diagram above, the input data consists of user and object characteristics and interactions. The engine takes these three pieces of information to build a model that classifies the appropriate interaction for each user. The sample data we obtained from Kaggle contained content that more or less met these requirements. What we need to do now is to convert the raw retail data into these three requirements.
We can use the powerful TensorRec Deep Learning server to run the training and test the prediction. We just need to make sure we provide clean and up-to-date information in feature matrices, and all business cases (such as removing long-tail products, offering only to active customers, custom category cart, etc.) can be added after validation. prediction by filtering data. .
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First, we load the transaction data shown above as a Pandas DataFrame fragment. We’ve made some changes to the column headings to make it easier to read, as well as to make it easier for other analysts and trading users. The change also allows for column name standardization across all other tables, providing seamless joins achieved through consistent column names.
Transaction data contains the essential details of a transaction: who, what, and how much. The table shows each customer’s transaction (Customer ID), date (Transaction Date), category (Product Catalog Code) and subcategory (Product Catalog Code), quantity (Quantity ), and sales amount (Total) of the item they purchased. the amount). Unfortunately, although we do not have the materials he purchased, we believe that the Unique Product Code, Additional Product Code, and Store Type will produce a unique material. This is probably the best option for now, and we’ll stick with it for the purposes of this exercise only. You can also create pseudo-random material for this exercise.
For the purposes of this exercise, we’ll change each unique combination of category, subcategory, and store type to carry a different element (the Material column).
The Kaggle repository also provides customer data, which is useful for us to identify customer characteristics. Is there enough information about this customer or is there more information we can glean from the data? Remember that functional engineering is critical to any successful ML algorithm, and this case is no different. Because TensorRec does not define requirements for object features and client features, we can take advantage of this freedom by using feature engineering to obtain improved object and client features. We can be as simple or complex as we want, so I’m going to change one thing: I’m going to use a generic marketing tool, RFMV, as an additional client feature.
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Thousands of items. Thousands of customers. Which box of cereal should I buy? Photo by Bernard Hermant on Unsplash
One of the goals of feature engineering is to extract features that are not overtly represented in the data, but rather to capture hidden patterns or correlations that may exist between them. To achieve this goal, a data scientist often uses unsupervised learning techniques such as K-Mean and T-SNe clustering in hopes of creating patterns, clusters, or insights that are not obvious by hand. Marketing analysis has benefited from this application by segmenting customers using an unsupervised clustering technique. The application extends beyond marketing; We may use this method as additional information to better understand our customers.
Among the many indicators available in the retail and consumer goods industry,