Tutorials & 101’s

Recommender systems have gained widespread attention in the research community, and as a result, there is a vast number of tutorials and introductory resources available online. A quick search on Google reveals hundreds, if not thousands, of tutorials designed to guide students, researchers, and developers through the core concepts and methodologies of building recommender systems.

However, not all tutorials are created equal. The quality, depth, and focus of these resources can vary significantly. For those new to the field or those looking to refine their knowledge, identifying reliable and well-structured resources is key. Below, we summarize a few notable tutorials and 101’s we have previously covered in our blog. These tutorials stand out either due to their comprehensive content, practical approach, or their alignment with the latest advancements in the field.

Comprehensive AI-generated Tutorial on Using RecBole for Recommender Systems

RecBole is a versatile, unified, and efficient library designed for prototyping and benchmarking recommender systems. Built on PyTorch, RecBole supports a variety of recommendation paradigms, including general recommendations, sequential recommendations, context-aware recommendations, and knowledge-based recommendations. This tutorial walks through the setup, configuration, and use of RecBole, explaining every design decision along the way. We will […]

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Creating a Recommender System Prototype Using LensKit and MovieLens

LensKit is a flexible Python library for creating, testing, and evaluating recommender systems. In this tutorial, we’ll guide you step-by-step to build a recommender system prototype using LensKit with the popular MovieLens dataset. By the end, you’ll have a functional model ready to recommend movies. LensKit is a Python-based open-source toolkit for building, testing, and […]

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Microsoft introduces a machine-learning lecture/tutorials

Microsoft launched a 12-week, 24-lesson curriculum for machine learning. They cover the standard topics like linear and logistic regression and clustering but also topics like Natural Language Processing and time series forecasting. The topics look interesting, though, personally, I am not sure about the format. Instead of videos, standard lecture slides or a book, the […]

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Building a content-based music recommender-system [Amol Mavuduru @TowardsDataScience]

Amol Mavuduru wrote a tutorial on building a content-based music recommender system with Spotify data. Have you ever wondered how Spotify recommends songs and playlists based on your listening history? Do you wonder how Spotify manages to find songs that sound similar to the ones you’ve already listened to? Interestingly, Spotify has a web API […]

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‘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://runningshoesforyou.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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