Take advantage of TensorFlow 2.0’s new flexible library to deploy a recommendation engine on retail dataset.

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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