User:Zeno Gantner

Zeno Gantner, formerly at University of Hildesheim, Germany. Now working at Nokia in Berlin. Primary developer of the MyMediaLite recommender system library. Co-organizer of the Recommender Stammtisch in Berlin.

• homepage
• Twitter: @zenogantner
• github: zenogantner
• StackOverflow: zenog
• Kaggle: zenog
• SlideShare: zenogantner

Article wishlist

• A/B testing
• active learning
• attribute-aware recommendation
• attribute-based recommendation
• 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)
• coclustering
• code recommendation [1]
• CofiRank (ask Markus)
• cold-start problem
• computational advertising
• content-based filtering
• context
• context-aware recommendation
• contextual bandit
• cross-validation [2]
• data analytics
• data mining
• decision theory (ask Martijn or Bart)
• 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
• Filter bubble (ask Alan and Neal)
• Flixster dataset
• F measure, F1 measure
• fold-in [3]
• GraphChi (ask Danny)
• GraphLab (ask Danny)
• Greg Linden
• grid search [4]
• group recommendation
• Harry Potter effect
• HCI
• higher-order SVD (ask Steffen)
• hybrid recommendation
• hyperparameter
• incentive
• 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 [5] (ask Noam)
• matrix factorization
• maximum a-priori estimation (MAP) (ask Christoph)
• mean average precision (MAP) - link to [6]
• 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 recommendation
• MyMedia (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 [7]
• 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 (ask Tobias)
• regularization
• reputation
• restricted Boltzmann machine (ask Andriy)
• review
• Ringo
• scalability (ask Sebastian)
• semi-supervised learning
• serendipity (ask Alan, ask Ben)
• similarity
• SmartTypes [8]
• 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
• SVD
• SVD++, 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
• Yahoo Movie Dataset (ask Noam and Markus)

RecSys people

• Joseph Konstan
• John Riedl
• Yehuda Koren
• Pearl Pu
• Greg Linden
• Paul Lamere
• Oscar Celma

Companies

• aklamio [9] (ask Robert)
• Alleyoop -- [10]
• Amazon
• Apple -- [11]
• BBC -- [12]
• BMAT (ask Oscar)
• Commendo (ask Michael)
• Directed Edge -- http://www.directededge.com
• EBay
• The Echo Nest [13] [14] (ask Paul Lamere)
• Facebook [15]
• Filmaster
• Filmtipset (thanks Alan)
• Flixster (thanks srbecker)
• foursquare -- [16] [17] (ask Max)
• Fredhopper (ask David)
• Google
• Gracenote (ask Oscar)
• Gravity
• Hulu
• Hunch
• Kaggle
• Knewton
• last.fm -- [18] [19]
• LinkedIn
• Lumi
• Microsoft (ask Noam and Markus)
• Moviepilot (thanks Alan)
• Myrrix (ask Sean)
• Netflix (ask Xavier)
• Nokia
• outbrain -- [20]
• Pandora [21] [22] (ask Tao)
• Plista (ask Andreas+Torben)
• Prudsys
• Recommind [23]
• RichRelevance (ask Darren)
• Samsung
• Scarab Research
• sematext
• Sidebar
• SoundCloud (ask Amelie and Michael)
• Spotify -- [24] [25]
• Strands
• TiVo
• Twitter [26]
• 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/