Resources

Recommender-System Software Libraries & APIs

There is a plethora of software libraries and APIs that often implement dozens of recommendation algorithms. We define a 'recommender-system software library' as e.g. a JAVA or Python library that you can easily integrate into your own application to run recommendation algorithms. Nevertheless, you still do need some recommender-system and programming knowledge to store data...

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List of RecSys Researchers

We hope to soon provide a directory of active recommender-system researchers and experts. Meanwhile, our tag cloud can help to identify experts in recommender systems. We tag every post with the names of relevant persons. For instance with those persons organizing a workshop, or publishing a book. Clicking on a name, e.g. https://recommender-systems.com/news/tag/francesco-ricci/ leads to...

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Datasets

There are a plethora of recommender-system datasets, and, more generally, almost every machine learning dataset can be used for recommendation systems, too. The de-facto standard dataset for recommendations is probably the MovieLens dataset (which exists in multiple variations). Based on a small study that we conducted, 40% of all research papers at the ACM Recommender...

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Computing Resources

Running recommender-system experiments may require significant computing resources. Especially memory-based approaches like kNN might need more memory than a normal desktop computer offers. Luckily, many organisations offer free or cheap (cloud) computing resources, though often limited in their free versions Cloud GPUs https://vast.ai/ claims to reduce GPU costs by factor 3-5 as they don´t offer...

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Competitions, Challenges, Shared Tasks & Living Labs

To test your skills in recommender-system development and research, competitions, shared tasks, and living labs are ideal. Notable initiatives include Kaggle is the major platform for machine learning competitions, which includes many recommender-systems competitions. The ACM RecSys Challenge is co-hosted every year alongside the ACM RecSys conference. It is sponsored by major industry partners who...

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Recommendations-As-a-Service (RaaS)

While recommender-system software libraries and APIs support you in developing a recommender system, a Recommender-System As-a-Service (RaaS) does all the work for you. Typically, you can integrate a recommender system into e.g. your e-commerce website or blog with a few clicks or lines of code. A RaaS comes with some compromises relating to flexibility, control...

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Prizes & Awards

NVIDIA Team wins the ACM RecSys Challenge 2020 It seems that NVIDIA becomes a new big player in the recommender-system community. After introducing Merlin – a Framework for Deep Recommender Systems – and offering an online course for Building Intelligent Recommender Systems, a team of NVIDIA employees now won the ACM RecSys Challenge 2020. The...

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