Difference between revisions of "User:Zeno Gantner"
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* [[Sidebar]] | * [[Sidebar]] | ||
* [[SoundCloud]] | * [[SoundCloud]] | ||
| − | * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] | + | * [[Spotify]] -- [http://paidcontent.org/2012/12/06/spotify-solves-discovery-by-discovering-music-aint-so-social-after-all/] [http://vimeo.com/57900625] [https://urldefense.com/v3/__https://www.wsj.com/video/series/wsj-explains/how-spotify-knows-what-you-want-to-hear-next/E91EB935-C3EE-42FF-B41A-246614F8F1A1?mod=tech_lead_pos5__;!!Bt8RZUm9aw!9jWj6_eMhtjp9bJ_vhQae7R843i0wMfn2_BVASWQvcyzT13IedsQDJaJiyNBokkz4L2UKM0H4C8I8s4T$] |
* [[Strands]] | * [[Strands]] | ||
* [[TiVo]] | * [[TiVo]] | ||
Revision as of 06:33, 23 May 2023
Zeno Gantner, formerly at University of Hildesheim, Germany. Now working at Zalando in Berlin. Primary developer of the MyMediaLite recommender system library. Co-organizer of the Recommender Stammtisch in Berlin.
homepage, Google Scholar, GitHub, StackOverflow, Kaggle, SlideShare
TODO
- page about Fashion RecSys workshop
- add link to Google tutorial
- add pages about PyTorch and TF recommendations
- marker templates for sequential recommendations, embeddings, e-commerce, CTR prediction, reinforcement learning, cold-start
- extend Person template: Google Scholar, LinkedIn, SlideShare, and GitHub
- extend/create dataset template (link to downloads, Google scholar search, Papers with Code)
- event/conference template (individual events and conference series)
- create company template (similar to persons), with github link, tech blog, Wikipedia link, corporate page, etc.
- page about Recsperts podcast
Article wishlist
- A/B testing
active learning- approximate nearest neighbor search
- attribute-aware recommendation
- attribute-based recommendation [1]
- autoencoder
- bag-of-items
- bagging
- bandit (-> multi-arm bandit)
- beer recommendation -- very important task ... (ask Ben)
blogs- BookCrossing (ask Cai-Nicolas)
- capped binomial deviation (CBD)
- Category:File format
- CHI (ask Alan)
- choice overload (ask Bart, Martijn, Dirk)
- click stream
- client-side recommendation (ask Chris)
- code recommendation [2]
- CofiRank (ask Markus)
cold-start problem- computational advertising
content-based filteringcontextcontext-aware recommendation- contextual bandit
- cross-validation [3]
- data analytics
- data mining
- decision theory
- deep learning
- distance
- distributed computing (ask Sebastian)
- distributed matrix factorization (ask Rainer)
- Eigentaste
- Epinions dataset
- Explanations (ask Nava)
- exploration vs. exploitation
- evaluation
- factorization model, factorization models
- FAQ for recommender system developers
- FAQ for recommender system users
- Fashion recommendation, Fashion recommendations
- Filter bubble (ask Alan and Neal)
- Flixster dataset
- F measure, F1 measure
- fold-in [4]
- GraphChi (ask Danny)
- GraphLab (ask Danny)
- Greg Linden
- grid search [5]
group recommendationHarry Potter effect- HCI
- higher-order SVD (ask Steffen)
hybrid recommendation- hyperparameter
- incentive
- Infer.NET [6]
- information retrieval
- Introduction to recommender systems
- Introduction to recommender system algorithms
- IPTV (ask Chris)
- 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 acronyms -- cmp. http://aclweb.org/aclwiki/index.php?title=Acronyms
- 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
- Matchbox [7] (ask Noam)
matrix factorization- maximum a-priori estimation (MAP)
- maximum inner product search
- mean average precision (MAP) - link to [8]
- mean reciprocal rank
Million Song DatasetMillion Song Dataset Challenge(ask Brian McFee)- MinHash
- [[MLOps]
- 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- neural networks
- news recommendation
- offline experiment
- one-class feedback
- overfitting
- page composition
- pairwise interaction tensor factorization (PITF, ask Steffen)
- Papers with Code
- parallel factor analysis (PARAFAC), canonical decomposition (ask Steffen)
- parallel matrix factorization
- parameter
Pearson correlation- personalization
- personalized advertising
- personalized prices [9]
- personalized search
- positive-only feedback
- preference elicitation (ask Martijn and Bart)
- product recommendation
- public transport (ask Neal)
- R
- ranking
- RecDB (ask, http://www-users.cs.umn.edu/~sarwat/RecDB/)
- recipe recommendation
- recommendation of financial products
- recommender lab (ask Michael H.)
recommender system- RecSys meetups (do it yourself)
- reinforcement learning (ask Tobias)
regularization- reputation
- restricted Boltzmann machine (ask Andriy)
- review
- Ringo
- scalability (ask Sebastian)
- semi-supervised learning
- sequential recommendation
- serendipity (ask Alan, ask Ben)
- session-based recommendation
- similarity
- SmartTypes [10]
- software as a service (ask Manuel B.)
- software recommendation
- standard benchmarks TODO
- state of the art cmp. http://aclweb.org/aclwiki/index.php?title=State_of_the_art
- stream processing
SVDSVD++, SVDPlusPlus- TaFeng
tag(thanks Alan)- 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
- user
- user-item matrix
- user model
- user preferences
- user recommendation
- user satisfaction
- video recommendation
- WSDM
- Yahoo Movie Dataset
RecSys people
- Joseph Konstan
- John Riedl
- Yehuda Koren
- Pearl Pu
- Greg Linden
- Paul Lamere
- Ted Dunning
- Sebastian Schelter -- https://scholar.google.de/citations?user=zCpQUukAAAAJ&hl=en -- https://github.com/sscdotopen -- https://github.com/schelterlabs
- Ralf Herbrich
Companies
- Alleyoop -- [11]
- BBC -- [12]
- BMAT
- Bol.com
- ByteDance and TikTok (redirect), [13], [14], [15], [16]
- Commendo
- Directed Edge -- http://www.directededge.com
- EBay
- The Echo Nest [17] [18] => Spotify
- Etsy
- foursquare -- [19] [20]
- Froomle
- Hunch
- Ikea
- Kaggle
- last.fm -- [21] [22]
- Lumi
- Microsoft
- Myrrix
- Nokia -- add 2011 Buzzwords presentation
- Otto
- outbrain -- [23]
- Pandora [24] [25] (ask Tao)
- Prudsys
- Recommind [26]
- RichRelevance (ask Darren)
- Samsung
- sematext
- ShareChat -- [27]
- Shopify -- ACM RecSys
- Sidebar
- SoundCloud
- Spotify -- [28] [29] [30]
- Strands
- TiVo
- Twitter [31] [32] [33] [34]
- Yahoo [35]
- YooChoose
- 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/
