Difference between revisions of "User:Zeno Gantner"
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| Line 16: | Line 16: | ||
* <s>[[content-based filtering]]</s> | * <s>[[content-based filtering]]</s> | ||
* [[choice overload]] | * [[choice overload]] | ||
| + | * [[context]] | ||
* [[context-aware recommendation]] | * [[context-aware recommendation]] | ||
* [[decision theory]] | * [[decision theory]] | ||
* [[Eigentaste]] | * [[Eigentaste]] | ||
| + | * [[exploration vs. exploitation]] | ||
* [[factorization models]] | * [[factorization models]] | ||
| + | * [[FAQ for recommender system users]] | ||
| + | * [[Filter bubble]] | ||
* [[group recommendation]] | * [[group recommendation]] | ||
* [[Harry Potter effect]] | * [[Harry Potter effect]] | ||
| Line 25: | Line 29: | ||
* <s>[[hybrid recommendation]]</s> | * <s>[[hybrid recommendation]]</s> | ||
* [[hyperparameter]] | * [[hyperparameter]] | ||
| + | * [[Introduction to recommender systems]] | ||
| + | * [[Introduction to recommender system algorithms]] | ||
* [[IPTV]] | * [[IPTV]] | ||
* [[item]] | * [[item]] | ||
| Line 31: | Line 37: | ||
* [[keyword-based recommendation]] | * [[keyword-based recommendation]] | ||
* <s>[[kNN]]</s> | * <s>[[kNN]]</s> | ||
| + | * [[learning]] | ||
* [[learning to rank]] | * [[learning to rank]] | ||
* [[location-aware recommendation]] | * [[location-aware recommendation]] | ||
| Line 46: | Line 53: | ||
* [[ranking]] | * [[ranking]] | ||
* [[recipe recommendation]] | * [[recipe recommendation]] | ||
| + | * [[recommendation of financial products]] | ||
* <s>[[recommender system]]</s> | * <s>[[recommender system]]</s> | ||
* <s>[[regularization]]</s> | * <s>[[regularization]]</s> | ||
| Line 60: | Line 68: | ||
* [[time-aware recommendation]] | * [[time-aware recommendation]] | ||
* [[Tucker decomposition]] | * [[Tucker decomposition]] | ||
| + | * [[UMAP]] | ||
* [[user]] | * [[user]] | ||
* [[user-item matrix]] | * [[user-item matrix]] | ||
Revision as of 06:10, 17 July 2011
Zeno Gantner from University of Hildesheim, Germany.
- homepage
- Twitter: @zenogantner
I am the main developer of the MyMediaLite recommender system library.
We have currently open PhD/PostDoc positions at our lab: Open positions at ISMLL 2011
Article wishlist
- A/B testing
active learning- attribute-aware recommendation
- attribute-based recommendation
- BookCrossing
cold-start problemcontent-based filtering- choice overload
- context
- context-aware recommendation
- decision theory
- Eigentaste
- exploration vs. exploitation
- factorization models
- FAQ for recommender system users
- Filter bubble
- group recommendation
- Harry Potter effect
- higher-order SVD
hybrid recommendation- hyperparameter
- Introduction to recommender systems
- Introduction to recommender system algorithms
- IPTV
- item
- Jester
- Joke recommendation
- keyword-based recommendation
kNN- learning
- learning to rank
- location-aware recommendation
matrix factorization- model
- news recommendation
- overfitting
- pairwise interaction tensor factorization
- parallel factor analysis (PARAFAC), canonical decomposition
- parameter
Pearson correlation- personalization
- personalized search
- product recommendation
- ranking
- recipe recommendation
- recommendation of financial products
recommender systemregularization- restricted Boltzmann machine
- scalability
- serendipity
- similarity
- SVD++
- TaFeng
- tag
- tag-aware recommendation
- tensor factorization
- text-based recommendation
- time-aware recommendation
- Tucker decomposition
- UMAP
- user
- user-item matrix
- user model
- user satisfaction
- video recommendation
- WSDM
Companies
- Amazon
- Commendo
- The Echo Nest
- Filmtipset
- Gravity
- Hunch
- Last.fm
- MoviePilot
- Netflix
- RichRelevance
- Scarab Research
- Strands
- TiVo
- Yahoo
