Tutorials & 101’s

There are probably hundreds if not thousands of recommender systems tutorials (see Google) and introductions to recommender systems (see Google). A few notable tutorials and 101’s, of which we reported in our blog, are listed in the remainder.

‘Lazy Predict’ Tutorial for AutoML with scikit-learn [Eryk Lewinson]

Eryk Lewinson wrote a tutorial on how to use Lazy Predict, which is an automated machine learning (AutoML) extension by Shankar Rao Pandala for scikit-learn. Automatisation is also becoming more prominent for recommender-systems (AutoRecSys) and hence this library could be useful for recommender-systems developers. While starting to work on a supervised learning problem, we are […]

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How can you build simple recommender systems with Surprise? [Amol Mavuduru]

Amol Mavuduru wrote a nice tutorial on how to build recommender systems with the Surprise Library. It should be noted, however, that Surprise is not being actively developed anymore. This is a pity, as Surprise is easy to use (I always recommend it to my students for their first experiments). As a side note, my […]

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A prototype of a “Similar Shoe” (Sneaker) Recommender-System with Source Code [Josh Barua]

Josh Barua created https://runningshoe4you.com/, a recommender-system for shoe recommendations. Users can provide as input either a specific shoe model or their preferences (comfort; weight; looks; …) and then receive 3 recommendations for shoes. Josh explains the recommender system in a blog post, and also releases the source code. It’s not a super sophisticated system but […]

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Techtiefen: Recommender Systems (German Podcast by Heise)

Heise Online, Germany’s probably best IT magazine, introduced a podcast series “Techtiefen” “for nerds from nerds”, already a while go. In its most recent episode, the journalist Nico Kreiling discusses the topic of recommender systems with Marcel Kurovski, Data Scientist at inovex. Recommendations, also Empfehlungen, sind mindestens so alt wie das Orakel von Delphi und […]

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Scalable Recommender Systems with NVTabular- A Fast Tabular Data Loading and Transformation Library [Ronay Ak @ Medium]

Ronay Ak et al. from RapidsAI wrote a nice in-depth post on how to use NVIDIA´s NVTabular to develop a recommender system that works with large amounts of data (1.3 TB). In this blog we will walk you through the NVTabular workflow steps in an example where we use ~1.3TB Criteo dataset shared by CriteoLabs for the predicting […]

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Building Personal Recommender Systems with Milvus and PaddlePaddle [Milvus@Medium]

In this article we use PaddlePaddle, a deep learning platform from Baidu, to build a model and combine Milvus, a vector similarity search engine, to build a personalized recommendation system that can quickly and accurately provide users with interesting information. Read more: https://medium.com/@milvusio/building-personal-recommender-systems-with-milvus-and-paddlepaddle-808567e3d65e

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Build a Recommender System in less than 60 minutes with ‘Amazon Personalize’ [Shivek Sachdev@Medium]

Shivek Sachdev wrote a tutorial on how to build a recommender-system with ‘Amazon Personalize’. Bad times! Ever since the Thai government has declared state of emergency over coronavirus — all of us were encouraged to work from home. I was assigned with a challenging task to conduct a webinar on the topic of “Increasing E-Commerce […]

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A Case For Embeddings in Recommendation Problems [Arnab Bhadury]

Once you have worked on different machine learning problems, most things in the field start to feel very similar. You take your raw input data, map it to a different latent space with fewer dimensions, and then perform your classification/regression/clustering. Recommender systems, new and old, are no different. In the classic collaborative filtering problem, you […]

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