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Welcome to RS_c, the central platform for the RecSys community. We provide curated lists of recommender-systems datasets, algorithms, books, conferences and many resources more. Maybe most importantly, we publish the latest recommender-system news. If you want your news to be reported on RS_c, read here.
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March 24, 2020

CARS@RecSys2020: Workshop on Context-Aware Recommender Systems

The primary goal of the CARS workshop is to revive the CARS topic and broadly discuss the main features of the next generation of CARS...
March 24, 2020

FairUMAP@UMAP2020: 3rd Workshop on Fairness in User Modeling, Adaptation, and Personalization

The 3rd Workshop on Fairness in User Modeling, Adaptation, and Personalization aims to bring together experts from academia and industry to discuss ethical, social, and...
March 24, 2020

MultiStePS@UMAP2020: Workshop on Personalization for Multi-stakeholder Public Services

Nowadays, with the digital revolution and the increasing popularity and adoption of e-government initiatives, public agencies from all over the world are implementing fully digital...
March 24, 2020

NeuRec@ICDM2020: Workshop on Advanced Neural Algorithms and Theories for Recommender Systems

NeuRec solicits the latest and significant contributions on developing and applying neural algorithms and theories for building intelligent recommender systems. Specifically, the workshop solicits papers...
March 9, 2020

Recommender Systems May Increase Citations [Scholarly Kitchen]

Recommender systems that promote related articles across publisher platforms increase citations according to a new study. The paper, The Citation Advantage of Promoted Articles in...
February 26, 2020

More than a decade of the ACM RecSys Conference [Alan Said, 2017]

This is already an old blog post from 2017, but still an intersting read. The eleventh edition of the ACM Conference on Recommender Systems (RecSys)...
September 24, 2019

WICRS2020@AAAI: Workshop on Interactive and Conversational Recommendation Systems

Recent years have seen an increase in work in interactive, sequential (e.g., session-based) interactions with recommender systems. Furthermore, the rise of conversational AI-based assistants in...

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News from Twitter

Do you want your news to appear here? Then add the hashtag #RS_c to your Tweet, and after one hour or so it will appear here automatically.
RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
11 Jan

Is "everything but p@k flawed" in #IR and #RecSys evaluation? Very interesting (and somewhat shocking) preprint by @NorbertFuhr et al. about IR metrics, which are also commonly used in recsys research. #RS_c

Norbert Fuhr@NorbertFuhr

Our comprehensive answer to the IR measures problem: https://arxiv.org/abs/2101.02668. 1. Everything but P@k is flawed. 2. Avoid Recall-based measures (R@k, AP, nDCG). 3. For all others, apply our transformation method.

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RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
11 Jan

E. Lewinson wrote a tutorial about Lazy Predict, an #AutoML lib by Shankar R. Pandala for #scikitlearn. AutoML is also becoming more prominent for recommender-systems (#AutoRecSys) and hence this library could be useful for #recsys developers. https://recommender-systems.com/news/2021/01/11/lazy-predict-scikit-learn-automl/ #RS_c

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RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
11 Jan

The @UvA_Amsterdam advertises an #AssistantProfessor position explicitly for #RecSys and #IR. Seems that #Amsterdam is getting serious about Recommender Systems (#Recsys2021 is also hosted in Amsterdam). https://www.uva.nl/en/content/vacancies/2020/12/20-765-tenure-track-position-in-information-retrieval.html #assistant_professor_recruitment #RS_c_jobs #RS_c

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seitlabSEIT Lab@seitlab·
14 Dec

The UbiCARS evaluation deadline has been extended! Please spend 10 minutes to give us your feedback on our research project, the Ubiquitous CARS MDD Framework: http://cs.ucy.ac.cy/seit/ubicars-evaluation/
(Feel free to share/forward the invitation!) #RS_c.

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RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
10 Dec

"Why isn’t your recommender system training faster on GPU?", a nice blog post by @Even_Oldridge and Nvidia with a comparison of #ComputerVision, #NLP, and #RecSys suitability for #GPUs https://recommender-systems.com/news/2020/12/09/why-isnt-your-recommender-system-training-faster-on-gpu-even-oldridge-nvidia/ #RS_c

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    Tag Cloud

    Alan Said Alexander Felfernig Alexander Tuzhilin Amra Delic arXiv Bamshad Mobasher Call For Papers (CfP) Ching-Wei Chen Covid-19 Deep Learning Dietmar Jannach Even Oldridge Forbes Francesco Ricci Frank Kane Gediminas Adomavicius Google Joseph A. Konstan Ludovico Boratto Marijn Koolen Markus Zanker Medium Michael Ekstrand Natural Language Processing (NLP) NeuRec NVIDIA NVIDIA Merlin Open Source Papers with Code Pre-Print Privacy RecSys Challenge RecSyS Conference Reinforcement Learning Sole Pera TechCrunch TensorFlow TowardsDataScience UMAP Walter Anelli

    Our Own Tweets

    https://twitter.com/RecSys_c
    Retweet on TwitterRecommender-Systems.com (RS_c) Retweeted
    DrCh0leSole Pera@DrCh0le·
    11 Jan

    Only a few more days till the abstract submission deadline (𝐉𝐚𝐧𝐮𝐚𝐫𝐲 𝟏𝟕) for @UMAPconf #PersonalizedRecSys track! @abellogin , @ludovicoboratto & I look forward to your #recsys submissions! https://um.org/umap2021/call-for-papers#personalizedrecsys

    Reply on Twitter 1348716389134069767Retweet on Twitter 134871638913406976712Like on Twitter 134871638913406976711Twitter 1348716389134069767
    RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
    11 Jan

    Is "everything but p@k flawed" in #IR and #RecSys evaluation? Very interesting (and somewhat shocking) preprint by @NorbertFuhr et al. about IR metrics, which are also commonly used in recsys research. #RS_c

    Norbert Fuhr@NorbertFuhr

    Our comprehensive answer to the IR measures problem: https://arxiv.org/abs/2101.02668. 1. Everything but P@k is flawed. 2. Avoid Recall-based measures (R@k, AP, nDCG). 3. For all others, apply our transformation method.

    Reply on Twitter 1348585852994277376Retweet on Twitter 1348585852994277376Like on Twitter 1348585852994277376Twitter 1348585852994277376
    RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
    11 Jan

    E. Lewinson wrote a tutorial about Lazy Predict, an #AutoML lib by Shankar R. Pandala for #scikitlearn. AutoML is also becoming more prominent for recommender-systems (#AutoRecSys) and hence this library could be useful for #recsys developers. https://recommender-systems.com/news/2021/01/11/lazy-predict-scikit-learn-automl/ #RS_c

    Reply on Twitter 1348581873874776065Retweet on Twitter 13485818738747760651Like on Twitter 13485818738747760653Twitter 1348581873874776065
    RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
    11 Jan

    #VersionControl for #RecSys and #MachineLearning is a topic that certainly deserves (more) attention. Good to see that #TensorFlow is starting doing exactly that with their new library ‘Machine Learning Metadata (MLMD)’ https://recommender-systems.com/news/2021/01/11/recommender-systems-version-control-tensorflow-mlmd/ #mlmd #DeepLearning #ML #DL

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    RecSys_cRecommender-Systems.com (RS_c)@RecSys_c·
    11 Jan

    The @UvA_Amsterdam advertises an #AssistantProfessor position explicitly for #RecSys and #IR. Seems that #Amsterdam is getting serious about Recommender Systems (#Recsys2021 is also hosted in Amsterdam). https://www.uva.nl/en/content/vacancies/2020/12/20-765-tenure-track-position-in-information-retrieval.html #assistant_professor_recruitment #RS_c_jobs #RS_c

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