Difference between revisions of "User:Zeno Gantner"
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* [[TaFeng]] | * [[TaFeng]] | ||
* <s>[[tag]]</s> (thanks Alan) | * <s>[[tag]]</s> (thanks Alan) | ||
| − | |||
* [[Tanimoto coefficient]] --> [[Jaccard index]] | * [[Tanimoto coefficient]] --> [[Jaccard index]] | ||
* [[Tapestry]] | * [[Tapestry]] | ||
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* [[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]] | ||
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* [[user satisfaction]] | * [[user satisfaction]] | ||
* [[video recommendation]] | * [[video recommendation]] | ||
| − | * | + | * [[WSDM]] |
| − | + | * [[Yahoo Movie Dataset]] | |
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| − | * [[Yahoo Movie Dataset]] | ||
=== RecSys people === | === RecSys people === | ||
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* [[Greg Linden]] | * [[Greg Linden]] | ||
* [[Paul Lamere]] | * [[Paul Lamere]] | ||
| − | * | + | * [[Ted Dunning]] |
=== Companies === | === Companies === | ||
Revision as of 08:50, 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
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/
