Knowing how to build a recommendation engine is an important milestone in a data scientist’s education. After all, recommendation engines power the hugely useful and profitable recommendation functionality of the e-commerce marketplace. Indeed, odds are you and your family have encountered something like the “Shopping for a new laptop? Here’s what people like you have bought in the past” scenario and benefited greatly.
Now, whether you are planning to join an e-commerce business as a data scientist or studying towards a Master’s degree in data science, knowing how to build a recommendation engine will open doors for you and certainly be something you end up doing. To help you onboard as quickly as possible, then, this guide is going to introduce you to some of the most useful concepts you can learn to quickly construct a recommendation engine using TensorFlow.
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Retail data has grown exponentially over these past few years. Even more so, Covid-19 has shifted massive number of transactions from offline to online. With an increase of data integration among mobile applications, notably of social media, companies have gained more insight into consumer’s activity, behavior, sentiment, and preference. How can we take advantage of these inputs to produce an effective, curated, and personalize recommendation engine that can cater real-time changes in the continuously ever-changing retail dynamics? We need not just a powerful engine that can cater massive text, time, and image data, but also a flexible library that can adapt to the fluctuations of these inputs.
To address this issue, I want to shed light on TensorFlow’s new recommendation library (TFRS), which has the potential to be scaled up to meet these challenges. My assumption is that it is still a library in progress, but as of now, they have released a few set of tools that allows us to build a hybrid engine, taking advantage of neural network’s embedding layers while simplifying the process of input and output. I would demonstrate a simple application of this library on an open retail dataset, with the goal to increase available recommendation tools among existing ones.
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If we plan to buy any new product, we normally ask our friends, research the product features, compare the product with similar products, read the product reviews on the internet and then we make our decision. How convenient if all this process was taken care of automatically and recommend the product efficiently? A recommendation engine or recommender system is the answer to this question.
Content-based filtering and collaborative-based filtering are the two popular recommendation systems. In this blog, we will see how we can build a simple content-based recommender system using Goodreads.com data.
Content-based recommendation systems recommend items to a user by using the similarity of items. This recommender system recommends products or items based on their description or features. It identifies the similarity between the products based on their descriptions. It also considers the user’s previous history in order to recommend a similar product.
Example: If a user likes the novel “Tell Me Your Dreams” by Sidney Sheldon, then the recommender system recommends the user to read other Sidney Sheldon novels, or it recommends a novel with the genre “non-fiction”. (Sidney Sheldon novels belong to the non-fiction genre).
As I mentioned above, we are using goodreads.com data and don’t have user reading history. Hence, we have used a simple content-based recommendation system. We are going to build two recommendation systems by using a book title and book description.
We need to find similar books to a given book and then recommend those similar books to the user. How do we find whether the given book is similar or dissimilar? A similarity measure was used to find this.
There are different similarity measures are available. Cosine Similarity was used in our recommender system to recommend the books. For more details on the similarity measure, please refer to this article.
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Our working final product can be tested here.
Have you ever wondered what a chatbot is and how to build one?
In this three-part series, we will teach you everything you need to build and deploy your Chatbot. By “we” here, I mean my team members (Ahmed, Dennis, Pedro, and Steven), four data science students at the Minerva Schools at KGI. The series will cover the following topics:
We use a Jupyter Python 3 notebook as a collaborative coding environment for this project, and other bits of code for the web app development and deployment. All the code for this series is available in this GitHub repository.
Businesses integrate chatbots into many processes and applications. You might need to interact with one while buying an item from Sephora, booking a flight from British Airways, or even customizing your cup of coffee from Starbucks. Developers build chatbots to understand customers’ needs and assist them without needing human help, making chatbots very useful for many customer-facing businesses. So how does a chatbot work?
Generally, there are three types of chatbots:
The chatbot we settled on creating is retrieval-based. Our bot can take a diverse set of responses, which are only slightly structured and output tailored recommendations. We had two main challenges to making this work: first, to build a flexible recommendation system in Python capable of taking in written requests by users and outputting decent recommendations. Second, implementing that algorithm in a web-app that is user-friendly and easy to use.
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From Amazon to Netflix to Pinterest, recommendation systems are the cornerstone of a majority of the modern-day billion-dollar industries. However, building recommender systems is not a straightforward task.
What if we can build them in a few lines? Dropping the nitty-gritty details and concentrating on implementing algorithms with more ease is what any data scientist would like to get their hands on. Abstraction is a common trait amongst popular machine learning libraries or frameworks like TensorFlow. Now, the team extends its services to recommender systems as well.
Google has introduced TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy.
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