Difference between revisions of "User:Zeno Gantner"
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| Line 14: | Line 14: | ||
* [[attribute-aware recommendation]] | * [[attribute-aware recommendation]] | ||
* [[attribute-based recommendation]] | * [[attribute-based recommendation]] | ||
| + | * [[bagging]] | ||
* [[bandit]] | * [[bandit]] | ||
| + | * [[beer recommendation]] -- very important task ... | ||
* [[blogs]] | * [[blogs]] | ||
* [[BookCrossing]] | * [[BookCrossing]] | ||
| Line 67: | Line 69: | ||
* <s>[[kNN]]</s> | * <s>[[kNN]]</s> | ||
* [[lab testing]] | * [[lab testing]] | ||
| + | * [[latency]] (ask Sebastian) | ||
* [[learning]] | * [[learning]] | ||
* [[learning to rank]] | * [[learning to rank]] | ||
| − | * [[live evaluation]] | + | * [[live evaluation]] (ask Andreas H./Alan) |
* [[location-aware recommendation]] | * [[location-aware recommendation]] | ||
| − | * [[long tail]] | + | * [[long tail]] (ask Oscar) |
* [[machine learning]] | * [[machine learning]] | ||
* [[MAP]] (ask Christoph) | * [[MAP]] (ask Christoph) | ||
| Line 91: | Line 94: | ||
* [[news recommendation]] | * [[news recommendation]] | ||
* [[offline experiment]] | * [[offline experiment]] | ||
| + | * [[one-class feedback]] | ||
* [[overfitting]] | * [[overfitting]] | ||
* [[pairwise interaction tensor factorization]] (ask Steffen) | * [[pairwise interaction tensor factorization]] (ask Steffen) | ||
| Line 99: | Line 103: | ||
* [[personalized advertising]] | * [[personalized advertising]] | ||
* [[personalized search]] | * [[personalized search]] | ||
| + | * [[positive-only feedback]] | ||
* [[preference elicitation]] | * [[preference elicitation]] | ||
* [[product recommendation]] | * [[product recommendation]] | ||
| Line 113: | Line 118: | ||
* [[restricted Boltzmann machine]] (ask Andriy) | * [[restricted Boltzmann machine]] (ask Andriy) | ||
* [[review]] | * [[review]] | ||
| − | * [[scalability]] | + | * [[scalability]] (ask Sebastian) |
* [[semi-supervised learning]] | * [[semi-supervised learning]] | ||
* [[serendipity]] (ask Alan) | * [[serendipity]] (ask Alan) | ||
Revision as of 09:01, 24 November 2011
Zeno Gantner from University of Hildesheim, Germany.
I am the primary developer of the MyMediaLite recommender system library.
TODO
- write a review/critique of this article: http://jmlr.csail.mit.edu/papers/v10/gunawardana09a.html
Article wishlist
- A/B testing
active learning- attribute-aware recommendation
- attribute-based recommendation
- bagging
- bandit
- beer recommendation -- very important task ...
- blogs
- BookCrossing
- Category:File format
- CHI
- choice overload
- click stream
- code recommendation [1]
- CofiRank (ask Markus)
cold-start problem- computational advertising
content-based filteringcontextcontext-aware recommendation- cross-validation
- data analytics
- data mining
- decision theory (ask Martijn or Bart)
- distance
- distributed computing
- distributed matrix factorization
- Eigentaste
- Epinions dataset
- exploration vs. exploitation
- factorization model, 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
- 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)
- learning
- learning to rank
- live evaluation (ask Andreas H./Alan)
- location-aware recommendation
- long tail (ask Oscar)
- machine learning
- MAP (ask Christoph)
- Markov chain (ask Christoph)
- Markov decision process, MDP
matrix factorization- mean average precision (MAP) - link to [2]
- mean reciprocal rank
- Million Song Dataset
- model
- monetization
- Movie Hack Day (ask Jannis)
- multi-arm bandit
- Music Hack Day
- music information retrieval (ask Oscar and/or Amelie)
music recommendation- MyMedia
NDCG- news recommendation
- offline experiment
- one-class feedback
- overfitting
- pairwise interaction tensor factorization (ask Steffen)
- parallel factor analysis (PARAFAC), canonical decomposition
- parameter
Pearson correlation- personalization
- personalized advertising
- 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- reinforcement learning
regularization- reputation
- restricted Boltzmann machine (ask Andriy)
- review
- scalability (ask Sebastian)
- semi-supervised learning
- serendipity (ask Alan)
- similarity
- software as a service
- software recommendation
SVDSVD++, SVDPlusPlus- TaFeng
- tag
- tag-aware recommendation (ask Karen or Leandro)
- Tanimoto coefficient --> Jaccard index
- 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
- Amazon
- Commendo
- EBay
- The Echo Nest
- Filmaster
- Filmtipset (ask Alan)
- Flixster
- foursquare
- Fredhopper
GravityHulu- Hunch
Knewton- last.fm [3]
LinkedIn- MoviePilot (ask Jannis)
- Netflix (ask Xavier)
- outbrain
- Pandora
- RichRelevance
- Scarab Research
- Strands
- TiVo
- Yahoo
- Zite
