Difference between revisions of "User:Zeno Gantner"
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* [[F measure]], [[F1 measure]] | * [[F measure]], [[F1 measure]] | ||
* [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf] | * [[fold-in]] [http://grouplens.org/papers/pdf/sarwar_SVD.pdf] | ||
| + | * [[GraphChi]] (ask Danny) | ||
* [[GraphLab]] (ask Danny) | * [[GraphLab]] (ask Danny) | ||
| + | * [[Greg Linden]] | ||
* <s>[[group recommendation]]</s> | * <s>[[group recommendation]]</s> | ||
* [[Harry Potter effect]] | * [[Harry Potter effect]] | ||
Revision as of 06:15, 19 September 2012
Zeno Gantner formerly at University of Hildesheim, Germany. Now working at Nokia.
I am the primary developer of the MyMediaLite recommender system library.
Article wishlist
- A/B testing
active learning- attribute-aware recommendation
- attribute-based recommendation
- bagging
- bandit
- beer recommendation -- very important task ...
blogs- BookCrossing
- capped binomial deviation (CBD)
- Category:File format
- CHI
- choice overload
- click stream
- coclustering
- code recommendation [1]
- CofiRank (ask Markus)
cold-start problem- computational advertising
content-based filteringcontextcontext-aware recommendation- contextual bandit
- cross-validation
- data analytics
- data mining
- decision theory (ask Martijn or Bart)
- distance
- distributed computing
- distributed matrix factorization
- Eigentaste
- Epinions dataset
- 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 [2]
- GraphChi (ask Danny)
- GraphLab (ask Danny)
- Greg Linden
group recommendation- Harry Potter effect
- HCI
- higher-order SVD
hybrid recommendation- hyperparameter
- incentive
- information retrieval
- Introduction to recommender systems
- Introduction to recommender system algorithms
- IPTV
- item
- IUI: IUI 2010, IUI 2011, IUI 2012
- Jaccard index
- Jester
- job recommendation
- Joke recommendation
- KDD Cup
- KDD: KDD 2007, KDD 2008, KDD 2009, KDD 2010
- KDD Cup 2010
- keyword-based recommendation
kNN- lab testing
- latency (ask Sebastian)
- latent factor model
- learning
- learning to rank
- 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
matrix factorization- maximum a-priori estimation (MAP) (ask Christoph)
- mean average precision (MAP) - link to [3]
- mean reciprocal rank
- Million Song Dataset (ask Paul Lamere)
- Million Song Dataset Challenge (
ask Brian McFee) - model
- monetization
- Movie Hack Day (ask Jannis and Alan)
- multi-arm bandit
- Music Hack Day
- 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
- parameter
Pearson correlation- personalization
- personalized advertising
- personalized prices [4]
- personalized search
- positive-only feedback
- preference elicitation
- product recommendation
- public transport (ask Neal)
- R
- ranking
- recipe recommendation
- recommendation of financial products
- recommender lab (ask Michael H.)
recommender system- RecSys meetups
- reinforcement learning
regularization- reputation
- restricted Boltzmann machine (ask Andriy)
- review
- Ringo
- scalability (ask Sebastian)
- semi-supervised learning
- serendipity (ask Alan)
- similarity
- SmartTypes [5]
- software as a service
- software recommendation
SVDSVD++, SVDPlusPlus- TaFeng
tag(thanks Alan)- tag-aware recommendation (ask Karen or Leandro)
- Tanimoto coefficient --> Jaccard index
- Tapestry
- tensor factorization
- text-based recommendation
- text mining
- time-aware recommendation
- transductive learning
- Tucker decomposition
- TV program recommendation
- UMAP: UMAP 2010,
UMAP 2011, UMAP 2012 - user
- user-item matrix
- user model
- user preferences
- user recommendation
- user satisfaction
- video recommendation
- web service
- 1st Workshop on Context-Aware Recommender Systems
- 2nd Workshop on Context-Aware Recommender Systems
- 3rd Workshop on Context-Aware Recommender Systems
- Workshop on Context-Aware Recommender Systems (CARS)
- WSDM: WSDM 2010, WSDM 2011, WSDM 2012
Companies
- Alleyoop -- [6]
- Amazon
- Apple -- [7]
- BBC -- [8]
- BMAT (ask Oscar)
- Commendo (ask Michael)
- Directed Edge -- http://www.directededge.com
- EBay
- The Echo Nest (ask Paul Lamere)
- Filmaster
Filmtipset(thanks Alan)- Flixster
- foursquare -- [9] [10] (ask Max)
- Fredhopper (ask David)
GravityHulu- Hunch
Knewton- last.fm -- [11] [12]
LinkedIn- Microsoft (ask Noam)
Moviepilot(thanks Alan)- Netflix (ask Xavier)
- Nokia
- outbrain -- [13]
- Pandora [14] (ask Tao)
- Plista (ask Andreas+Torben)
- Prudsys
- Recommind [15]
- RichRelevance
- Samsung
- Scarab Research
- sematext
- Sidebar
- Strands
- TiVo
- Twitter [16]
- 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/
