Difference between revisions of "User:Zeno Gantner"
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| Line 12: | Line 12: | ||
* [[attribute-based recommendation]] | * [[attribute-based recommendation]] | ||
* [[bagging]] | * [[bagging]] | ||
| − | * [[bandit]] | + | * [[bandit]] (-> [[multi-arm bandit]]) |
* [[beer recommendation]] -- very important task ... | * [[beer recommendation]] -- very important task ... | ||
* <s>[[blogs]]</s> | * <s>[[blogs]]</s> | ||
| Line 35: | Line 35: | ||
* [[decision theory]] (ask Martijn or Bart) | * [[decision theory]] (ask Martijn or Bart) | ||
* [[distance]] | * [[distance]] | ||
| − | * [[distributed computing]] | + | * [[distributed computing]] (ask Sebastian) |
* [[distributed matrix factorization]] | * [[distributed matrix factorization]] | ||
* [[Eigentaste]] | * [[Eigentaste]] | ||
| Line 63: | Line 63: | ||
* [[IPTV]] | * [[IPTV]] | ||
* [[item]] | * [[item]] | ||
| − | * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]] | + | * [[IUI]]: [[IUI 2010]], [[IUI 2011]], [[IUI 2012]], [[IUI 2013]] |
* [[Jaccard index]] | * [[Jaccard index]] | ||
* [[Jester]] | * [[Jester]] | ||
* [[job recommendation]] | * [[job recommendation]] | ||
* [[Joke recommendation]] | * [[Joke recommendation]] | ||
| − | * [[KDD Cup]] | + | * [[KDD Cup]]: [[KDD Cup 2010]] [[KDD Cup 2011]] [[KDD Cup 2012]] |
* [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]] | * [[KDD]]: [[KDD 2007]], [[KDD 2008]], [[KDD 2009]], [[KDD 2010]] | ||
| − | |||
* [[keyword-based recommendation]] | * [[keyword-based recommendation]] | ||
* <s>[[kNN]]</s> | * <s>[[kNN]]</s> | ||
| Line 95: | Line 94: | ||
* [[monetization]] | * [[monetization]] | ||
* [[Movie Hack Day]] (ask Jannis and Alan) | * [[Movie Hack Day]] (ask Jannis and Alan) | ||
| − | * [[multi-arm bandit]] | + | * [[multi-arm bandit]] (ask Matt) |
* [[Music Hack Day]] (ask Amelie) | * [[Music Hack Day]] (ask Amelie) | ||
* [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus) | * [[music information retrieval]] (ask Oscar, Ben, Amelie, Markus) | ||
| Line 106: | Line 105: | ||
* [[overfitting]] | * [[overfitting]] | ||
* [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen) | * [[pairwise interaction tensor factorization]] ([[PITF]], ask Steffen) | ||
| − | * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] | + | * [[parallel factor analysis]] ([[PARAFAC]]), [[canonical decomposition]] (ask Steffen) |
| + | * [[parallel matrix factorization]] | ||
* [[parameter]] | * [[parameter]] | ||
* <s>[[Pearson correlation]]</s> | * <s>[[Pearson correlation]]</s> | ||
| Line 114: | Line 114: | ||
* [[personalized search]] | * [[personalized search]] | ||
* [[positive-only feedback]] | * [[positive-only feedback]] | ||
| − | * [[preference elicitation]] | + | * [[preference elicitation]] (ask Martijn and Bart) |
* [[product recommendation]] | * [[product recommendation]] | ||
* [[public transport]] (ask Neal) | * [[public transport]] (ask Neal) | ||
| Line 132: | Line 132: | ||
* [[scalability]] (ask Sebastian) | * [[scalability]] (ask Sebastian) | ||
* [[semi-supervised learning]] | * [[semi-supervised learning]] | ||
| − | * [[serendipity]] (ask Alan) | + | * [[serendipity]] (ask Alan, ask Ben) |
* [[similarity]] | * [[similarity]] | ||
* [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets] | * [[SmartTypes]] [http://www.smarttypes.org/blog/graphlab_datasets] | ||
| Line 144: | Line 144: | ||
* [[Tanimoto coefficient]] --> [[Jaccard index]] | * [[Tanimoto coefficient]] --> [[Jaccard index]] | ||
* [[Tapestry]] | * [[Tapestry]] | ||
| − | * [[tensor factorization]] | + | * [[tensor factorization]] (ask Steffen) |
* [[text-based recommendation]] | * [[text-based recommendation]] | ||
* [[text mining]] | * [[text mining]] | ||
* [[time-aware recommendation]] | * [[time-aware recommendation]] | ||
* [[transductive learning]] | * [[transductive learning]] | ||
| − | * [[Tucker decomposition]] | + | * [[Tucker decomposition]] (ask Steffen) |
| − | * [[TV program recommendation]] | + | * [[TV program recommendation]] (ask Chris) |
* [[UMAP]]: [[UMAP 2010]], <s>[[UMAP 2011]]</s>, [[UMAP 2012]] | * [[UMAP]]: [[UMAP 2010]], <s>[[UMAP 2011]]</s>, [[UMAP 2012]] | ||
* [[user]] | * [[user]] | ||
| Line 160: | Line 160: | ||
* [[video recommendation]] | * [[video recommendation]] | ||
* [[web service]] | * [[web service]] | ||
| − | * [[1st Workshop on Context-Aware Recommender Systems]] | + | * [[1st Workshop on Context-Aware Recommender Systems]] (ask Alan) |
| − | * [[2nd Workshop on Context-Aware Recommender Systems]] | + | * [[2nd Workshop on Context-Aware Recommender Systems]] (ask Alan) |
| − | * [[3rd Workshop on Context-Aware Recommender Systems]] | + | * [[3rd Workshop on Context-Aware Recommender Systems]] (ask Alan) |
| − | * [[Workshop on Context-Aware Recommender Systems]] ([[CARS]]) | + | * [[Workshop on Context-Aware Recommender Systems]] ([[CARS]], ask Alan) |
| − | * [[WSDM]]: [[WSDM 2010]], [[WSDM 2011]], [[WSDM 2012]] | + | * [[WSDM]]: [[WSDM 2010]], [[WSDM 2011]], [[WSDM 2012]], [[WSDM 2013]] |
=== Companies === | === Companies === | ||
Revision as of 16:26, 20 December 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 (-> multi-arm bandit)
- beer recommendation -- very important task ...
blogs- BookCrossing (ask Cai-Nicolas)
- 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 (ask Sebastian)
- 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 recommendationHarry 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, 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 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 (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 [4]
- personalized search
- positive-only feedback
- preference elicitation (ask Martijn and Bart)
- product recommendation
- public transport (ask Neal)
- R
- ranking
- recipe recommendation
- recommendation of financial products
- recommender lab (ask Michael H.)
recommender system- RecSys meetups (do it yourself)
- reinforcement learning
regularization- reputation
- restricted Boltzmann machine (ask Andriy)
- review
- Ringo
- scalability (ask Sebastian)
- semi-supervised learning
- serendipity (ask Alan, ask Ben)
- 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 (ask Steffen)
- text-based recommendation
- text mining
- time-aware recommendation
- transductive learning
- Tucker decomposition (ask Steffen)
- TV program recommendation (ask Chris)
- 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 (ask Alan)
- 2nd Workshop on Context-Aware Recommender Systems (ask Alan)
- 3rd Workshop on Context-Aware Recommender Systems (ask Alan)
- Workshop on Context-Aware Recommender Systems (CARS, ask Alan)
- WSDM: WSDM 2010, WSDM 2011, WSDM 2012, WSDM 2013
Companies
- aklamio [6] (ask Robert)
- Alleyoop -- [7]
- Amazon
- Apple -- [8]
- BBC -- [9]
- BMAT (ask Oscar)
- Commendo (ask Michael)
- Directed Edge -- http://www.directededge.com
- EBay
- The Echo Nest [10] [11] (ask Paul Lamere)
- Facebook [12]
- Filmaster
Filmtipset(thanks Alan)- Flixster
- foursquare -- [13] [14] (ask Max)
- Fredhopper (ask David)
GravityHulu- Hunch
- Kaggle
Knewton- last.fm -- [15] [16]
LinkedIn- Lumi
- Microsoft (ask Noam)
Moviepilot(thanks Alan)- Netflix (ask Xavier)
- Nokia
- outbrain -- [17]
- Pandora [18] [19] (ask Tao)
- Plista (ask Andreas+Torben)
- Prudsys
- Recommind [20]
- RichRelevance
- Samsung
- Scarab Research
- sematext
- Sidebar
- Spotify -- [21]
- Strands
- TiVo
- Twitter [22]
- 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/
