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

The Google Map below shows experts in the field of recommender systems. Our goal is to keep this map up-to-date with senior and active recommender-systems researchers. The map provides a good starting point for prospective Ph.D. students looking for potential supervisors; organizations and businesses looking for recommender-systems experts for joint projects or consultancy jobs, and...

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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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https://Your-Project.Recommender-Systems.com

If you have a project related to recommender systems, we would be glad to support you with web space and a sub-domain like https://Your-Project.Recommender-Systems.com. Alternatively, you may get only a subdomain that forwards to your website at some other website host (e.g. GitHub or GoDaddy). If you are interested, please contact us, and let us...

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