Difference between revisions of "User:Zeno Gantner"

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Zeno Gantner from University of Hildesheim, Germany.
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[[Zeno Gantner]], formerly at University of Hildesheim, Germany. Now working at [[Zalando]] in [[Berlin]].
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* [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage]
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Primary developer of the [[MyMediaLite]] recommender system library.
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* Twitter: [http://twitter.com/zenogantner @zenogantner]
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Co-organizer of the [[Recommender Stammtisch]] in Berlin.
  
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I am the main developer of the [[MyMediaLite]] recommender system library.
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[http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage], [https://scholar.google.com/citations?user=AhVYsaoAAAAJ Google Scholar], [https://github.com/zenogantner/ GitHub], [http://stackoverflow.com/users/404824/zenog StackOverflow], [http://www.kaggle.com/users/15462/zenog Kaggle], [http://www.slideshare.net/zenogantner SlideShare]
  
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We have currently open PhD/PostDoc positions at [http://ismll.de our lab]: [[Open positions at ISMLL 2011]]
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== TODO ==
 +
 
 +
* page about Fashion RecSys workshop
 +
* add link to Google tutorial
 +
* add pages about PyTorch and TF recommendations
 +
* marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start
 +
* extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub
 +
* extend/create dataset template (link to downloads, Google scholar search, Papers with Code)
 +
* event/conference template (individual events and conference series)
 +
* create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.
 +
* page about [[Recsperts podcast]]
  
 
== Article wishlist ==
 
== Article wishlist ==
 
* [[A/B testing]]
 
* [[A/B testing]]
 
* <s>[[active learning]]</s>
 
* <s>[[active learning]]</s>
 +
* [[approximate nearest neighbor search]]
 
* [[attribute-aware recommendation]]
 
* [[attribute-aware recommendation]]
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* [[attribute-based recommendation]]
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* [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/]
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* [[BookCrossing]]
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* [[autoencoder]]
 +
* [[bag-of-items]]
 +
* [[bagging]]
 +
* [[bandit]] (-> [[multi-arm bandit]])
 +
* [[beer recommendation]] -- very important task ... (ask Ben)
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* <s>[[blogs]]</s>
 +
* [[BookCrossing]] (ask Cai-Nicolas)
 +
* [[capped binomial deviation]] ([[CBD]])
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* [[:Category:File format]]
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* [[CHI]] (ask Alan)
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* [[choice overload]] (ask Bart, Martijn, Dirk)
 +
* [[click stream]]
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* [[client-side recommendation]] (ask Chris)
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* [[code recommendation]] [http://t.co/QakdUh02]
 +
* [[CofiRank]] (ask Markus)
 
* <s>[[cold-start problem]]</s>
 
* <s>[[cold-start problem]]</s>
 +
* [[computational advertising]]
 
* <s>[[content-based filtering]]</s>
 
* <s>[[content-based filtering]]</s>
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* [[choice overload]]
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* <s>[[context]]</s>
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* [[context-aware recommendation]]
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* <s>[[context-aware recommendation]]</s>
 +
* [[contextual bandit]]
 +
* [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)]
 +
* [[data analytics]]
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* [[data mining]]
 
* [[decision theory]]
 
* [[decision theory]]
 +
* [[deep learning]]
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* [[distance]]
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* [[distributed computing]] (ask Sebastian)
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* [[distributed matrix factorization]] (ask Rainer)
 
* [[Eigentaste]]
 
* [[Eigentaste]]
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* [[factorization models]]
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* [[Epinions dataset]]
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* [[group recommendation]]
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* [[Explanations]] (ask Nava)
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* [[Harry Potter effect]]
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* [[exploration vs. exploitation]]
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* [[higher-order SVD]]
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* [[evaluation]]
 +
* [[factorization model]], [[factorization models]]
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* [[FAQ for recommender system developers]]
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* [[FAQ for recommender system users]]
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* [[Fashion recommendation]], [[Fashion recommendations]]
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* [[Filter bubble]] (ask Alan and Neal)
 +
* [[Flixster dataset]]
 +
* [[F measure]], [[F1 measure]]
 +
* [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf]
 +
* [[GraphChi]] (ask Danny)
 +
* [[GraphLab]] (ask Danny)
 +
* [[Greg Linden]]
 +
* [[grid search]] [http://en.wikipedia.org/wiki/Grid_search]
 +
* <s>[[group recommendation]]</s>
 +
* <s>[[Harry Potter effect]]</s>
 +
* [[HCI]]
 +
* [[higher-order SVD]] (ask Steffen)
 
* <s>[[hybrid recommendation]]</s>
 
* <s>[[hybrid recommendation]]</s>
 
* [[hyperparameter]]
 
* [[hyperparameter]]
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* [[IPTV]]
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* [[incentive]]
 +
* [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx]
 +
* [[information retrieval]]
 +
* [[Introduction to recommender systems]]
 +
* [[Introduction to recommender system algorithms]]
 +
* [[IPTV]] (ask Chris)
 
* [[item]]
 
* [[item]]
 +
* [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]]
 +
* [[Jaccard index]]
 
* [[Jester]]
 
* [[Jester]]
 +
* [[job recommendation]]
 
* [[Joke recommendation]]
 
* [[Joke recommendation]]
 +
* [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]]
 +
* [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]]
 
* [[keyword-based recommendation]]
 
* [[keyword-based recommendation]]
 
* <s>[[kNN]]</s>
 
* <s>[[kNN]]</s>
 +
* [[lab testing]]
 +
* [[latency]]  (ask Sebastian)
 +
* [[latent factor model]]
 +
* [[learning]]
 
* [[learning to rank]]
 
* [[learning to rank]]
 +
* [[List of acronyms]] -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms
 +
* [[List of recommender system meetings]]
 +
* [[live evaluation]] (ask Andreas H./Alan)
 
* [[location-aware recommendation]]
 
* [[location-aware recommendation]]
 +
* [[London RecSys Meetup]] (ask Neal)
 +
* [[long tail]] (ask Oscar)
 +
* [[machine learning]]
 +
* [[Markov chain]] (ask Christoph)
 +
* [[Markov decision process]], [[MDP]]
 +
* [[Matchbox]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20System.aspx] (ask Noam)
 
* <s>[[matrix factorization]]</s>
 
* <s>[[matrix factorization]]</s>
 +
* [[maximum a-priori estimation]] ([[MAP]])
 +
* [[maximum inner product search]]
 +
* [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision]
 +
* [[mean reciprocal rank]]
 +
* <s>[[Million Song Dataset]]</s>
 +
* <s>[[Million Song Dataset Challenge]]</s> (<s>ask Brian McFee</s>)
 +
* [[MinHash]]
 +
* [[MLOps]
 
* [[model]]
 
* [[model]]
 +
* [[monetization]]
 +
* [[Movie Hack Day]] (ask Jannis and Alan)
 +
* [[multi-arm bandit]] (ask Matt)
 +
* [[Music Hack Day]] (ask Amelie)
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* [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus)
 +
* <s>[[music recommendation]]</s>
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* <s>[[MyMedia]]</s> (thank you Alan!)
 +
* <s>[[NDCG]]</s>
 +
* [[neural networks]]
 
* [[news recommendation]]
 
* [[news recommendation]]
 +
* [[offline experiment]]
 +
* [[one-class feedback]]
 
* [[overfitting]]
 
* [[overfitting]]
−
* [[pairwise interaction tensor factorization]]
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* [[page composition]]
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* [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]]
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* [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen)
 +
* [[Papers with Code]]
 +
* [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen)
 +
* [[parallel matrix factorization]]
 
* [[parameter]]
 
* [[parameter]]
 
* <s>[[Pearson correlation]]</s>
 
* <s>[[Pearson correlation]]</s>
 
* [[personalization]]
 
* [[personalization]]
 +
* [[personalized advertising]]
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* [[personalized prices]] [http://blogs.smartmoney.com/advice/2012/06/26/how-online-retailers-personalize-prices/]
 
* [[personalized search]]
 
* [[personalized search]]
 +
* [[positive-only feedback]]
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* [[preference elicitation]] (ask Martijn and Bart)
 
* [[product recommendation]]
 
* [[product recommendation]]
 +
* [[public transport]] (ask Neal)
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* [[R]]
 
* [[ranking]]
 
* [[ranking]]
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* [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)
 
* [[recipe recommendation]]
 
* [[recipe recommendation]]
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* [[recommender system]]
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* [[recommendation of financial products]]
 +
* [[recommender lab]] (ask Michael H.)
 +
* <s>[[recommender system]]</s>
 +
* [[RecSys meetups]] (do it yourself)
 +
* [[reinforcement learning]] (ask Tobias)
 
* <s>[[regularization]]</s>
 
* <s>[[regularization]]</s>
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* [[restricted Boltzmann machine]]
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* [[reputation]]
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* [[scalability]]
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* [[restricted Boltzmann machine]] (ask Andriy)
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* [[serendipity]]
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* [[review]]
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* [[Ringo]]
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* [[scalability]] (ask Sebastian)
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* [[semi-supervised learning]]
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* [[sequential recommendation]]
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* [[serendipity]] (ask Alan, ask Ben)
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* [[session-based recommendation]]
 
* [[similarity]]
 
* [[similarity]]
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* [[SVD++]]
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* [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets]
 +
* [[software as a service]] (ask Manuel B.)
 +
* [[software recommendation]]
 +
* [[standard benchmarks]] TODO
 +
* [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art
 +
* [[stream processing]]
 +
* <s>[[SVD]]</s>
 +
* <s>[[SVD++]], [[SVDPlusPlus]]</s>
 
* [[TaFeng]]
 
* [[TaFeng]]
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* [[tag]]
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* <s>[[tag]]</s> (thanks Alan)
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* [[tag-aware recommendation]]
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* [[Tanimoto coefficient]] --> [[Jaccard index]]
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* [[tensor factorization]]
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* [[Tapestry]]
 +
* [[tensor factorization]] (ask Steffen)
 
* [[text-based recommendation]]
 
* [[text-based recommendation]]
 +
* [[text mining]]
 
* [[time-aware recommendation]]
 
* [[time-aware recommendation]]
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* [[Tucker decomposition]]
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* [[transductive learning]]
 +
* [[Tucker decomposition]] (ask Steffen)
 +
* [[TV program recommendation]] (ask Chris)
 +
* [[UMAP]]
 
* [[user]]
 
* [[user]]
 
* [[user-item matrix]]
 
* [[user-item matrix]]
 
* [[user model]]
 
* [[user model]]
 +
* [[user preferences]]
 +
* [[user recommendation]]
 
* [[user satisfaction]]
 
* [[user satisfaction]]
 
* [[video recommendation]]
 
* [[video recommendation]]
 +
* [[WSDM]]
 +
* [[Yahoo Movie Dataset]]
 +
 +
=== RecSys people ===
 +
 +
* [[Joseph Konstan]]
 +
* [[John Riedl]]
 +
* [[Yehuda Koren]]
 +
* [[Pearl Pu]]
 +
* [[Greg Linden]]
 +
* [[Paul Lamere]]
 +
* [[Ted Dunning]]
 +
* [[Sebastian Schelter]] -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs
 +
* [[Ralf Herbrich]]
  
 
=== Companies ===
 
=== Companies ===
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* [[Amazon]]
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* [[Alleyoop]] -- [http://gigaom.com/2012/06/06/with-new-recommendation-engine-alleyoop-wants-to-be-a-tutor-for-teens]
 +
* [[Baidu]] -- [http://www.yichang-cs.com/jlu/WSDM21_unbiased.pdf]
 +
* [[BBC]] -- [http://www.bbc.co.uk/blogs/researchanddevelopment/2012/05/client-side-recommendations.shtml]
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* [[BMAT]]
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* [[Bol.com]]
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* [[Booking.com]] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762238_Personalization_in_Practice_Methods_and_Applications/links/60409bf04585154e8c75323d/Personalization-in-Practice-Methods-and-Applications.pdf] [https://dl.acm.org/doi/pdf/10.1145/3383313.3412215] [https://dl.acm.org/doi/abs/10.1145/3460231.3474611] [https://dl.acm.org/doi/abs/10.1145/3511808.3557100] [https://dl.acm.org/doi/abs/10.1145/3292500.3330744] [http://www.toinebogers.com/workshops/complexrec2020/Mavridis.pdf] [https://arxiv.org/abs/2109.06723] [https://dl.acm.org/doi/abs/10.1145/3460231.3474611]
 +
* '''[[ByteDance]]''' and [[TikTok]]/[[Douyin]] (redirect), [https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you], [https://github.com/bytedance/Hammer], [https://github.com/bytedance/byteps], [https://github.com/bytedance/LargeBatchCTR], [https://dl.acm.org/doi/pdf/10.1145/3308558.3313447] [https://arxiv.org/pdf/2204.06240.pdf]
 
* [[Commendo]]
 
* [[Commendo]]
−
* [[The Echo Nest]]
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* [[Directed Edge]] -- http://www.directededge.com
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* [[Filmtipset]]
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* [[EBay]]
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* [[Google]]
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* [[The Echo Nest]] [http://blog.echonest.com/post/33229165293/taste-profiles-go-public] [http://notes.variogr.am/post/37675885491/how-music-recommendation-works-and-doesnt-work] => [[Spotify]]
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* [[Gravity]]
+
* [[Etsy]]
 +
* [[foursquare]] -- [http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/] [http://engineering.foursquare.com/2012/03/23/machine-learning-with-large-networks-of-people-and-places/]
 +
* [[Froomle]]
 
* [[Hunch]]
 
* [[Hunch]]
−
* [[Last.fm]]
+
* [[Ikea]]
−
* [[MoviePilot]]
+
* [[Kaggle]]
−
* [[Netflix]]
+
* [[Kuaishou]] [https://en.wikipedia.org/wiki/Kuaishou], [https://arxiv.org/abs/2302.01724], [https://arxiv.org/pdf/2212.02779.pdf]
−
* [[RichRelevance]]
+
* [[last.fm]] -- [http://www.slideshare.net/MarkLevy/algorithms-on-hadoop-at-lastfm] [http://www.quora.com/How-does-Last-fm-compute-lists-of-similar-artists]
 +
* [[Lumi]]
 +
* [[Microsoft]]
 +
* [[Myrrix]]
 +
* [[Nokia]] -- add 2011 Buzzwords presentation
 +
* [[Otto]]
 +
* [[outbrain]] -- [http://www.conversationagent.com/2012/05/content-recommendation-engine-outbrain.html]
 +
* [[Pandora]] [http://www.fastcompany.com/1808123/tom-conrad-pandora-music-genome-project] [http://blog.pandora.com/pandora/archives/2012/10/pandora-and-art.html] (ask Tao)
 +
* [[Pinterest]] -- [https://arxiv.org/pdf/1702.07969.pdf]
 +
* [[Prudsys]]
 +
* [[Recommind]] [http://www.recommind.com/]
 +
* [[RichRelevance]] (ask Darren)
 +
* [[Samsung]]
 +
* [[sematext]]
 +
* [[ShareChat]] -- [https://dl.acm.org/doi/abs/10.1145/3543873.3587679]
 +
* [[Shopify]] -- ACM RecSys
 +
* [[Sidebar]]
 +
* [[SoundCloud]]
 +
* [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$]
 
* [[Strands]]
 
* [[Strands]]
 
* [[TiVo]]
 
* [[TiVo]]
−
* [[Yahoo]]
+
* [[Twitter]] [http://engineering.twitter.com/2012/03/generating-recommendations-with.html] [https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm] [https://github.com/twitter/the-algorithm] [https://github.com/twitter/sbf]
 +
* '''[[Yahoo]]''' [https://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf]
 +
* [[Yandex]] [https://openreview.net/pdf?id=MXfTQp8bZF]
 +
* [[YooChoose]]
 +
* [[Zite]]
 +
 
 +
== RecSys slides, classes, etc. ==
 +
* http://www.lsi.dsc.ufcg.edu.br/lib/exe/fetch.php?id=bd_lanche&cache=cache&media=fatoracao_matrizes.pdf
 +
* [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/ Berkeley: Practical Machine Learning]: [http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/collaborative/slides.pdf collaborative filtering] (only rating prediction)
 +
* http://alex.smola.org/teaching/berkeley2012/recommender.html
 +
* http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/

Latest revision as of 05:39, 28 September 2023

Zeno Gantner, formerly at University of Hildesheim, Germany. Now working at Zalando in Berlin. Primary developer of the MyMediaLite recommender system library. Co-organizer of the Recommender Stammtisch in Berlin.

homepage, Google Scholar, GitHub, StackOverflow, Kaggle, SlideShare

TODO

  • page about Fashion RecSys workshop
  • add link to Google tutorial
  • add pages about PyTorch and TF recommendations
  • marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start
  • extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub
  • extend/create dataset template (link to downloads, Google scholar search, Papers with Code)
  • event/conference template (individual events and conference series)
  • create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.
  • page about Recsperts podcast

Article wishlist

RecSys people

Companies

RecSys slides, classes, etc.

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