Difference between revisions of "User:Zeno Gantner"
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− | [[Zeno Gantner]] formerly at University of Hildesheim, Germany. Now working at [[ | + | [[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. | |
− | |||
− | + | ||
+ | [[Open positions at Zalando 2020]] | ||
+ | |||
+ | |||
+ | [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] | ||
+ | |||
+ | |||
+ | == TODO == | ||
+ | |||
+ | * article about [[Zalando]] | ||
+ | * article about Fashion RecSys workshop | ||
+ | * add ISMLL dissertations | ||
+ | * extend Person template | ||
+ | * extend/create dataset template | ||
== Article wishlist == | == Article wishlist == | ||
Line 10: | Line 22: | ||
* <s>[[active learning]]</s> | * <s>[[active learning]]</s> | ||
* [[attribute-aware recommendation]] | * [[attribute-aware recommendation]] | ||
− | * [[attribute-based recommendation]] | + | * [[attribute-based recommendation]] [http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/] |
* [[bagging]] | * [[bagging]] | ||
* [[bandit]] (-> [[multi-arm bandit]]) | * [[bandit]] (-> [[multi-arm bandit]]) | ||
Line 21: | Line 33: | ||
* [[choice overload]] (ask Bart, Martijn, Dirk) | * [[choice overload]] (ask Bart, Martijn, Dirk) | ||
* [[click stream]] | * [[click stream]] | ||
+ | * [[client-side recommendation]] (ask Chris) | ||
* [[coclustering]] | * [[coclustering]] | ||
* [[code recommendation]] [http://t.co/QakdUh02] | * [[code recommendation]] [http://t.co/QakdUh02] | ||
Line 30: | Line 43: | ||
* <s>[[context-aware recommendation]]</s> | * <s>[[context-aware recommendation]]</s> | ||
* [[contextual bandit]] | * [[contextual bandit]] | ||
− | * [[cross-validation]] | + | * [[cross-validation]] [http://en.wikipedia.org/wiki/Cross-validation_(statistics)] |
* [[data analytics]] | * [[data analytics]] | ||
* [[data mining]] | * [[data mining]] | ||
Line 36: | Line 49: | ||
* [[distance]] | * [[distance]] | ||
* [[distributed computing]] (ask Sebastian) | * [[distributed computing]] (ask Sebastian) | ||
− | * [[distributed matrix factorization]] | + | * [[distributed matrix factorization]] (ask Rainer) |
* [[Eigentaste]] | * [[Eigentaste]] | ||
* [[Epinions dataset]] | * [[Epinions dataset]] | ||
Line 52: | Line 65: | ||
* [[GraphLab]] (ask Danny) | * [[GraphLab]] (ask Danny) | ||
* [[Greg Linden]] | * [[Greg Linden]] | ||
+ | * [[grid search]] [http://en.wikipedia.org/wiki/Grid_search] | ||
* <s>[[group recommendation]]</s> | * <s>[[group recommendation]]</s> | ||
* <s>[[Harry Potter effect]]</s> | * <s>[[Harry Potter effect]]</s> | ||
Line 59: | Line 73: | ||
* [[hyperparameter]] | * [[hyperparameter]] | ||
* [[incentive]] | * [[incentive]] | ||
+ | * [[Infer.NET]] [http://research.microsoft.com/en-us/um/cambridge/projects/infernet/docs/Recommender%20system.aspx] | ||
* [[information retrieval]] | * [[information retrieval]] | ||
* [[Introduction to recommender systems]] | * [[Introduction to recommender systems]] | ||
Line 87: | Line 102: | ||
* [[Markov chain]] (ask Christoph) | * [[Markov chain]] (ask Christoph) | ||
* [[Markov decision process]], [[MDP]] | * [[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]]) (ask Christoph) | * [[maximum a-priori estimation]] ([[MAP]]) (ask Christoph) | ||
* [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision] | * [[mean average precision]] ([[MAP]]) - link to [http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision] | ||
* [[mean reciprocal rank]] | * [[mean reciprocal rank]] | ||
− | * [[Million Song Dataset]] | + | * <s>[[Million Song Dataset]]</s> |
* <s>[[Million Song Dataset Challenge]]</s> (<s>ask Brian McFee</s>) | * <s>[[Million Song Dataset Challenge]]</s> (<s>ask Brian McFee</s>) | ||
+ | * [[MinHash]] | ||
* [[model]] | * [[model]] | ||
* [[monetization]] | * [[monetization]] | ||
Line 121: | Line 138: | ||
* [[R]] | * [[R]] | ||
* [[ranking]] | * [[ranking]] | ||
+ | * [[RecDB]] (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/) | ||
* [[recipe recommendation]] | * [[recipe recommendation]] | ||
* [[recommendation of financial products]] | * [[recommendation of financial products]] | ||
Line 141: | Line 159: | ||
* [[standard benchmarks]] TODO | * [[standard benchmarks]] TODO | ||
* [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art | * [[state of the art]] cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art | ||
+ | * [[stream processing]] | ||
* <s>[[SVD]]</s> | * <s>[[SVD]]</s> | ||
* <s>[[SVD++]], [[SVDPlusPlus]]</s> | * <s>[[SVD++]], [[SVDPlusPlus]]</s> | ||
* [[TaFeng]] | * [[TaFeng]] | ||
* <s>[[tag]]</s> (thanks Alan) | * <s>[[tag]]</s> (thanks Alan) | ||
− | |||
* [[Tanimoto coefficient]] --> [[Jaccard index]] | * [[Tanimoto coefficient]] --> [[Jaccard index]] | ||
* [[Tapestry]] | * [[Tapestry]] | ||
Line 155: | Line 173: | ||
* [[Tucker decomposition]] (ask Steffen) | * [[Tucker decomposition]] (ask Steffen) | ||
* [[TV program recommendation]] (ask Chris) | * [[TV program recommendation]] (ask Chris) | ||
− | * [[UMAP | + | * [[UMAP]] |
* [[user]] | * [[user]] | ||
* [[user-item matrix]] | * [[user-item matrix]] | ||
Line 163: | Line 181: | ||
* [[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 | ||
=== Companies === | === Companies === | ||
Line 185: | Line 208: | ||
* [[Filmaster]] | * [[Filmaster]] | ||
* <s>[[Filmtipset]]</s> (thanks Alan) | * <s>[[Filmtipset]]</s> (thanks Alan) | ||
− | * [[Flixster]] | + | * <s>[[Flixster]]</s> (thanks srbecker) |
* [[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/] (ask Max) | * [[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/] (ask Max) | ||
* [[Fredhopper]] (ask David) | * [[Fredhopper]] (ask David) | ||
Line 214: | Line 237: | ||
* [[Sidebar]] | * [[Sidebar]] | ||
* [[SoundCloud]] (ask Amelie and Michael) | * [[SoundCloud]] (ask Amelie and Michael) | ||
− | * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] | + | * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] |
* [[Strands]] | * [[Strands]] | ||
* [[TiVo]] | * [[TiVo]] |
Revision as of 08:52, 28 September 2020
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.
Open positions at Zalando 2020
homepage, Google Scholar, GitHub, StackOverflow, Kaggle, SlideShare
TODO
- article about Zalando
- article about Fashion RecSys workshop
- add ISMLL dissertations
- extend Person template
- extend/create dataset template
Article wishlist
- A/B testing
active learning- attribute-aware recommendation
- attribute-based recommendation [1]
- bagging
- bandit (-> multi-arm bandit)
- beer recommendation -- very important task ... (ask Ben)
blogs- BookCrossing (ask Cai-Nicolas)
- capped binomial deviation (CBD)
- Category:File format
- CHI (ask Alan)
- choice overload (ask Bart, Martijn, Dirk)
- click stream
- client-side recommendation (ask Chris)
- coclustering
- code recommendation [2]
- CofiRank (ask Markus)
cold-start problem- computational advertising
content-based filteringcontextcontext-aware recommendation- contextual bandit
- cross-validation [3]
- data analytics
- data mining
- decision theory (ask Martijn or Bart)
- distance
- distributed computing (ask Sebastian)
- distributed matrix factorization (ask Rainer)
- Eigentaste
- Epinions dataset
- Explanations (ask Nava)
- exploration vs. exploitation
- evaluation
- factorization model, factorization models
- FAQ for recommender system developers
- FAQ for recommender system users
- Filter bubble (ask Alan and Neal)
- Flixster dataset
- F measure, F1 measure
- fold-in [4]
- GraphChi (ask Danny)
- GraphLab (ask Danny)
- Greg Linden
- grid search [5]
group recommendationHarry Potter effect- HCI
- higher-order SVD (ask Steffen)
hybrid recommendation- hyperparameter
- incentive
- Infer.NET [6]
- information retrieval
- Introduction to recommender systems
- Introduction to recommender system algorithms
- IPTV (ask Chris)
- item
- IUI: IUI 2010, IUI 2011, IUI 2012, IUI 2013
- Jaccard index
- Jester
- job 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
kNN- lab testing
- latency (ask Sebastian)
- latent factor model
- learning
- 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
- London RecSys Meetup (ask Neal)
- long tail (ask Oscar)
- machine learning
- Markov chain (ask Christoph)
- Markov decision process, MDP
- Matchbox [7] (ask Noam)
matrix factorization- maximum a-priori estimation (MAP) (ask Christoph)
- mean average precision (MAP) - link to [8]
- mean reciprocal rank
Million Song DatasetMillion Song Dataset Challenge(ask Brian McFee)- MinHash
- model
- monetization
- Movie Hack Day (ask Jannis and Alan)
- multi-arm bandit (ask Matt)
- Music Hack Day (ask Amelie)
- music information retrieval (ask Oscar, Ben, Amelie, Markus)
music recommendationMyMedia(thank you Alan!)NDCG- news recommendation
- offline experiment
- one-class feedback
- overfitting
- pairwise interaction tensor factorization (PITF, ask Steffen)
- parallel factor analysis (PARAFAC), canonical decomposition (ask Steffen)
- parallel matrix factorization
- parameter
Pearson correlation- personalization
- personalized advertising
- personalized prices [9]
- personalized search
- positive-only feedback
- preference elicitation (ask Martijn and Bart)
- product recommendation
- public transport (ask Neal)
- R
- ranking
- RecDB (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)
- recipe recommendation
- recommendation of financial products
- recommender lab (ask Michael H.)
recommender system- RecSys meetups (do it yourself)
- reinforcement learning (ask Tobias)
regularization- reputation
- restricted Boltzmann machine (ask Andriy)
- review
- Ringo
- scalability (ask Sebastian)
- semi-supervised learning
- serendipity (ask Alan, ask Ben)
- similarity
- SmartTypes [10]
- 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
SVDSVD++, SVDPlusPlus- TaFeng
tag(thanks Alan)- Tanimoto coefficient --> Jaccard index
- Tapestry
- tensor factorization (ask Steffen)
- text-based recommendation
- text mining
- time-aware recommendation
- transductive learning
- Tucker decomposition (ask Steffen)
- TV program recommendation (ask Chris)
- UMAP
- user
- user-item matrix
- user model
- user preferences
- user recommendation
- user satisfaction
- 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
Companies
- aklamio [11] (ask Robert)
- Alleyoop -- [12]
- Amazon
- Apple -- [13]
- BBC -- [14]
- BMAT (ask Oscar)
- Commendo (ask Michael)
- Directed Edge -- http://www.directededge.com
- EBay
- The Echo Nest [15] [16] (ask Paul Lamere)
- Facebook [17]
- Filmaster
Filmtipset(thanks Alan)Flixster(thanks srbecker)- foursquare -- [18] [19] (ask Max)
- Fredhopper (ask David)
- Gracenote (ask Oscar)
GravityHulu- Hunch
- Kaggle
Knewton- last.fm -- [20] [21]
LinkedIn- Lumi
- Microsoft (ask Noam and Markus)
Moviepilot(thanks Alan)- Myrrix (ask Sean)
- Netflix (ask Xavier)
- Nokia
- outbrain -- [22]
- Pandora [23] [24] (ask Tao)
- Plista (ask Andreas+Torben)
- Prudsys
- Recommind [25]
- RichRelevance (ask Darren)
- Samsung
- Scarab Research
- sematext
- Sidebar
- SoundCloud (ask Amelie and Michael)
- Spotify -- [26] [27]
- Strands
- TiVo
- Twitter [28]
- Yahoo
- YooChoose (ask David)
- Zalando (ask Peter/Lina/Tobias/Ulf)
- 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
- Berkeley: Practical Machine Learning: collaborative filtering (only rating prediction)
- http://alex.smola.org/teaching/berkeley2012/recommender.html
- http://cms.uni-konstanz.de/informatik/rendle/teaching/ss2012/fm0/