This is an applied research report by Cloudera Fast Forward. We write reports about emerging technologies, and conduct experiments to explore what's possible. Read our full report on Session-based Recommender Systems below, or download the PDF, and be sure to check out our github repo for the Experiments section. Being able to recommend an item of interest to a user (based on their past preferences) is a highly relevant problem in practice. A key trend over the past few years has been session-based recommendation algorithms that provide recommendations solely based on a user's interactions in an ongoing session, and which do not require the existence of user profiles or their entire historical preferences. This report explores a simple, yet powerful, NLP-based approach (word2vec) to recommend a next item to a user. While NLP-based approaches are generally employed for linguistic tasks, here we exploit them to learn the structure induced by a user's behavior or an item's nature. Recommendation systems have become a cornerstone of modern life, spanning sectors that include online retail, music and video streaming, and even content publishing. These systems help us navigate the sheer volume of content on the internet, allowing us to discover what's interesting or important to us. When implemented correctly, recommendation systems help us navigate efficiently and make more informed decisions. While this report is not comprehensive, we will touch on a variety of approaches to recommendation systems, and dig deep into one approach in particular. We'll demonstrate how we used that approach to build a recommendation system from the ground up for an e-commerce use case, and showcase our experimental findings. Recommendation systems are not new, and they have already achieved great success over the past ten years through a variety of approaches. These classic recommendation systems can be broadly categorized as content-based, as collaborative filtering-based, or as hybrid approaches that combine aspects of the two. At a high level, content-based filtering makes recommendations based on user preferences for product features, as identified through either the user's previous actions or explicit feedback.
Drive-thru is a popular sales channel in the fast food industry where consumers can make food purchases without leaving their cars. Drive-thru recommendation systems allow restaurants to display food recommendations on the digital menu board as guests are making their orders. Popular recommendation models in eCommerce scenarios rely on user attributes (such as user profiles or purchase history) to generate recommendations, while such information is hard to obtain in the drive-thru use case. Thus, in this paper, we propose a new recommendation model Transformer Cross Transformer (TxT), which exploits the guest order behavior and contextual features (such as location, time, and weather) using Transformer encoders for drive-thru recommendations. Empirical results show that our TxT model achieves superior results in Burger King's drive-thru production environment compared with existing recommendation solutions. In addition, we implement a unified system to run end-to-end big data analytics and deep learning workloads on the same cluster. We find that in practice, maintaining a single big data cluster for the entire pipeline is more efficient and cost-saving. Our recommendation system is not only beneficial for drive-thru scenarios, and it can also be generalized to other customer interaction channels.
A couple of months ago I left Pivotal to join idealo.de Besides the usual tasks like building out the data science team, setting up the infrastructure and many more administrative stuff, I had to define the ML powered product roadmap. And associated with this was also the definition of a Minimum Viable Product (MVP) for machine learning products. The question I often face though, here at idealo and actually also back at my time at Pivotal, is what actually a good MVP means? In this article, I will shed some lights on the different dimensions of a good MVP for machine learning products drawing in the experiences that I've gained so far.
IT and analytics managers struggling with all the data flooding into their organizations may find it hard to ignore the increased marketing push machine learning tools are getting from technology vendors. And for good reason: Running automated algorithms designed to learn on their own as they churn through large data sets can accelerate data mining and predictive analytics applications -- and give users information they might not get otherwise. But companies looking to take advantage of machine learning often face a substantial learning curve. For starters, a lot of big data infrastructure technologies -- Hadoop, the Spark processing engine and related open source software in particular -- typically underlie machine learning efforts. In many cases, that means building a suitable data processing and management architecture from scratch.
"When you think about recommending something to someone, there's a real business reason why you might want to do that." Machine learning recommendation systems are not just a trendy feature of online stores. It is a mighty tool that can propel your business to the next level, if used strategically. No wonder Jack Chua suggests always having "a great tie-in to the underlying KPI of what you want to drive". If you're still hesitating on how exactly to use recommendations to invigorate your business, we invite you to learn from the experience of those who already made it work brilliantly! We collected the best examples of machine learning implementation in recommenders (including our own development projects) and explain in plain English how to build a machine learning recommender systems from scratch. Okay, let's start with a short quiz.