Difference between revisions of "User:Zeno Gantner"
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Zeno Gantner from University of Hildesheim, Germany. | Zeno Gantner from University of Hildesheim, Germany. | ||
* [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage] | * [http://www.ismll.uni-hildesheim.de/personen/gantner_en.html homepage] | ||
| − | * | + | * [http://twitter.com/zenogantner Twitter: @zenogantner] |
| + | * [https://github.com/zenogantner/ github: zenogantner] | ||
I am the main developer of the [[MyMediaLite]] recommender system library. | I am the main developer of the [[MyMediaLite]] recommender system library. | ||
| − | |||
| − | |||
== Article wishlist == | == Article wishlist == | ||
| Line 13: | Line 12: | ||
* [[attribute-based recommendation]] | * [[attribute-based recommendation]] | ||
* [[bandit]] | * [[bandit]] | ||
| + | * [[blogs]] | ||
* [[BookCrossing]] | * [[BookCrossing]] | ||
* [[:Category:File format]] | * [[:Category:File format]] | ||
| Line 26: | Line 26: | ||
* [[data mining]] | * [[data mining]] | ||
* [[decision theory]] (ask Martijn or Bart) | * [[decision theory]] (ask Martijn or Bart) | ||
| + | * [[distance]] | ||
* [[distributed computing]] | * [[distributed computing]] | ||
* [[distributed matrix factorization]] | * [[distributed matrix factorization]] | ||
| Line 85: | Line 86: | ||
* [[personalized advertising]] | * [[personalized advertising]] | ||
* [[personalized search]] | * [[personalized search]] | ||
| + | * [[preference elicitation]] | ||
* [[product recommendation]] | * [[product recommendation]] | ||
* [[public transport]] (ask Neal) | * [[public transport]] (ask Neal) | ||
| Line 98: | Line 100: | ||
* [[review]] | * [[review]] | ||
* [[scalability]] | * [[scalability]] | ||
| − | * [[serendipity]] | + | * [[serendipity]] (ask Alan) |
* [[similarity]] | * [[similarity]] | ||
* [[software as a service]] | * [[software as a service]] | ||
| Line 117: | Line 119: | ||
* [[user-item matrix]] | * [[user-item matrix]] | ||
* [[user model]] | * [[user model]] | ||
| + | * [[user preferences]] | ||
* [[user recommendation]] | * [[user recommendation]] | ||
* [[user satisfaction]] | * [[user satisfaction]] | ||
| Line 132: | Line 135: | ||
* [[Flixster]] | * [[Flixster]] | ||
* [[Google]] | * [[Google]] | ||
| − | * [[Gravity]] | + | * <s>[[Gravity]]</s> |
| − | * [[Hulu]] | + | * <s>[[Hulu]]</s> |
* [[Hunch]] | * [[Hunch]] | ||
* [[Last.fm]] | * [[Last.fm]] | ||
Revision as of 11:45, 23 September 2011
Zeno Gantner from University of Hildesheim, Germany.
I am the main developer of the MyMediaLite recommender system library.
Article wishlist
- A/B testing
active learning- attribute-aware recommendation
- attribute-based recommendation
- bandit
- blogs
- BookCrossing
- Category:File format
- CHI
- choice overload
- CofiRank
cold-start problem- computational advertising
content-based filteringcontextcontext-aware recommendation- data analytics
- data mining
- decision theory (ask Martijn or Bart)
- distance
- distributed computing
- distributed matrix factorization
- Eigentaste
- Epinions dataset
- exploration vs. exploitation
- factorization models
- FAQ for recommender system developers
- FAQ for recommender system users
- Filter bubble (ask Alan and Neal)
- Flixster dataset
- GraphLab (ask Danny)
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
- Jester
- Joke recommendation
- KDD Cup
- KDD: KDD 2007, KDD 2008, KDD 2009, KDD 2010
- KDD Cup 2010
- keyword-based recommendation
kNN- learning
- learning to rank
- location-aware recommendation
- long tail
- machine learning
- MAP (ask Christoph)
- Markov chain (ask Christoph)
- Markov decision process, MDP
matrix factorization- mean average precision (MAP) - link to [1]
- mean reciprocal rank
- Million Song Dataset
- model
- monetization
- Movie Hack Day (ask Jannis)
- multi-arm bandit
- Music Hack Day
- MyMedia
NDCG- news recommendation
- overfitting
- pairwise interaction tensor factorization (ask Steffen)
- parallel factor analysis (PARAFAC), canonical decomposition
- parameter
Pearson correlation- personalization
- personalized advertising
- personalized search
- preference elicitation
- product recommendation
- public transport (ask Neal)
- R
- ranking
- recipe recommendation
- recommendation of financial products
recommender system- reinforcement learning
regularization- reputation
- restricted Boltzmann machine (ask Andriy)
- review
- scalability
- serendipity (ask Alan)
- similarity
- software as a service
- software recommendation
- SVD
- SVD++, SVDPlusPlus
- TaFeng
- tag
- tag-aware recommendation
- tensor factorization
- text-based recommendation
- text mining
- time-aware recommendation
- 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
- WSDM: WSDM 2010, WSDM 2011, WSDM 2012
Companies
- Amazon
- Commendo
- EBay
- The Echo Nest
- Filmtipset
- Flixster
GravityHulu- Hunch
- Last.fm
- MoviePilot
- Netflix
- Pandora
- RichRelevance
- Scarab Research
- Strands
- TiVo
- Yahoo
RecSys slides, classes, etc.
Personal TODO list
- rename remaining plural categories
- (after some time) remove the old plural categories
