ACM Recommender Systems 2012: Most discussed, tweeted papers & presentations #RecSys2012. Blog reviews. Datasets. Social Graph. Links

 

Recsys 2012 – (Probably) Most commented and tweeted papers & presentations ACM Recommender Systems 2012 #recsys2012

 

Building Large-scale Real-world Recommender System-Tutorial

Xavier Amatriain @xamat @Netflix

http://www.slideshare.net/xamat/building-largescale-realworld-recommender-systems-recsys2012-tutorial

 

Content, Connections, and Context

Daniel Tunkelang @dtunkelang @LinkedIn

http://www.slideshare.net/dtunkelang/content-connections-and-context?ref=http://thenoisychannel.com/2012/09/09/content-connections-and-context/

 

Spotting Trends: The Wisdom of the Few   

Xiaolan Sha, Pietro Michiardi @michiard and Matteo Dell’Amico @matteodellamico @EURECOM

Daniele Quercia @danielequercia @Cambridge_Uni

http://www.cl.cam.ac.uk/~dq209/publications/sha12spotting.pdf

 

CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering

Yue Shi and Linas Baltrunas at @Delft University of Technology

Alexandros Karatzoglou @telefonicaid

http://www.ci.tuwien.ac.at/~alexis/Publications_files/climf-recsys12.pdf

 

How users evaluate each other in Social Media

Jure Leskovec  @jure @Stanford University

http://i.stanford.edu/~jure/pub/talks/evals-recsys-sep12.pdf

 

Inspectability and Control in Social Recommenders

Bart Knijnenburg @usabart @UCIrvine

http://www.slideshare.net/usabart/inspectability-and-control-in-social-recommenders

 

Mendeley Suggest: Engineering a Personalised Article Recommender System

Kris Jack @_krisjack @Mendeley

http://www.slideshare.net/KrisJack/mendeley-suggest-engineering-a-personalised-article-recommender-system

 

Pareto-Efficient Hybridization for Multi-Objective Recommender Systems

Marco Tulio Ribeiro and Adriano Veloso UF Minas Gerais

Anisio Lacerda and Nivio Ziviani @Zunnit Technologies

http://www.slideshare.net/marcotulioribeiro54/presentation-recsys12

 

Tutorial on Conducting User Experiments in Recommender Systems

Bart Knijnenburg @usabart @UCIrvine

http://www.slideshare.net/mobile/usabart/tutorial-on-conducting-user-experiments-in-recommender-systems

 

I’ve got 10 million songs in my pocket. Now what?

Paul Lamere @plamere @echonest

http://www.slideshare.net/plamere/ive-got-10-million-songs-in-my-pocket-now-what

 

Online Controlled Experiments: Introduction, Learnings, and Humbling Statistics

Ron Kohavi @Microsoft

http://www.exp-platform.com/Documents/2012-08%20Puzzling%20Outcomes%20KDD.pptx

http://www.exp-platform.com/Documents/2012-09%20ACMRecSysNR.pdf

 

Ads and the City: Considering Geographic Distance Goes Long Way

Diego Saez-Trumper @univpompeufabra

Daniele Quercia @danielequercia and Jon Crowcroftx  @tforcworc @Cambridge_Uni

http://www.slideshare.net/daniele.quercia/recsys12-3

 

Enhancement of the Neutrality in Recommendation

Toshihiro Kamishima, Shotaro Akaho, and Hideki Asoh @AIST Tsukuba

Jun Sakuma @University of Tsukuba

http://recex.ist.tugraz.at/RecSysWorkshop2012/01_Kamishima_et_al.pdf

 

Get Lost: GetLostBot! Annoying People with Serendipitous Recommendations

Ben Kirman @benki @University of Lincoln

http://www.slideshare.net/bkirman/get-lost-getlostbot-annoying-people-with-serendipitous-recommendations

 

Trustworthy Online Controlled Experiments: Five Puzzing Outcomes Explained

Ron Kohavi, Alex Deng, Brian Frasca, Roger Longbotham, Toby Walker, Ya Xu

@MSFTResearch

http://www.exp-platform.com/Documents/2012-08%20Puzzling%20Outcomes%20KDD.pptx

http://www.exp-platform.com/Documents/puzzlingOutcomesInControlledExperiments.pdf

 

Contextualizing Useful Recommendations

Francesco Ricci University of Bozen-Bolzano

http://www.inf.unibz.it/~ricci/Slides/Context-UMAP-2012-Ricci.pdf

 

Iterative Smoothing Technique for Improving Stability of Recommender Systems

Gediminas Adomavicius @University of Minnesota

Jingjing Zhang @Inidan University

http://ir.ii.uam.es/rue2012/papers/rue2012-adomavicius.pdf

 

Using Crowdsourcing to Compare Document Recommendation Strategies for Conversations

Maryam Habibi and Andrei Popescu-Belis @Idiap Research Institute

http://ir.ii.uam.es/rue2012/papers/rue2012-habibi.pdf

 

Insights on Social Recommender System

Wolney L. de Mello Neto @Vrije Universiteit Brussel

Ann Nowé @Vrije Universiteit Brussel

http://ir.ii.uam.es/rue2012/papers/rue2012-demello.pdf

 

 

Blog Reviews of #RecSys2012

 

RecSys2012: A long (and likely biased) summary

Xavier Amatriain @xamat @Netflix

http://technocalifornia.blogspot.co.uk/2012/09/recsys-2012-long-and-likely-biased.html

 

RecSys 2012: Beyond Five Stars

Daniel Tunkelang @dtunkelang @LinkedIn

http://thenoisychannel.com/2012/09/14/recsys-2012-beyond-five-stars/

 

RecSys 2012: few things remember

Daniele Quercia @danielequercia @Cambridge_Un

http://www.syslog.cl.cam.ac.uk/2012/09/14/recsys-2012-few-things-i-remember/

 

Recsys 20102 Conference- A personal summary

Marcel Blattner @FFHS

http://lwsffhs.wordpress.com/2012/09/17/recsys2012-conference-a-personal-summary/

 

 

Most mentioned twitter accounts during #RecSys12

http://li500-250.members.linode.com/recsys12/recommender/recsys2012_1/corerankings/mentions

 

Twitter interactions and Social Graph During #RecSys2012

https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=1191

 

 

Systems, Datasets, and Toolsets at #RecSys2012

 

Clique: Graph & Network Analysis Cluster

Padraig Cunningham @PadraigC @University College Dublin

http://cliquecluster.org/files/Overview-PlansV3.pdf

 

Matchbox: Large Scale Bayesian Recommendations -Microsoft Research

David Stern, Ralf Herbrich, Thore Graepel @MSFTResearch

http://research.microsoft.com/pubs/79460/www09.pdf

 

Infer.NET framework for running Bayesian inference in graphical models

@MSFTResearch

http://research.microsoft.com/en-us/um/cambridge/projects/infernet/

 

KONECT – The Koblenz Dataset Collection

Dr. Jérôme Kunegis @kunegis @University of Koblenz- Landau

http://konect.uni-koblenz.de/networks

 

HetRec 2011 Data Sets | GroupLens Research

GroupLens @University of Minnesota and IR Group @uam_madrid

http://www.grouplens.org/node/462

 

LensKit Recommender Framework — LensKit Recommender Implementation

GroupLens @University of Minnesota

http://lenskit.grouplens.org/index.html

 

Movielens- helping you find the right movies

GroupLens @University of Minnesota

http://movielens.umn.edu/login

 

myPersonality Project: Research, Datasets and Papers on personality

Michal Kosinski and David Stillwell Psychometrics Centre @Cambridge_Uni

http://mypersonality.org/wiki/doku.php?id=start

 

 

Other papers and presentations #RecSys2012 mentioned and discussed

 

Case Study Evaluation of Mahout as a Recommender Platform

Carlos E. Seminario and David C. Wilson @University of North Carolina Charlotte

http://ir.ii.uam.es/rue2012/papers/rue2012-seminario.pdf

 

Evaluating Various Implicit Factors in E-commerce

Ladislav Peska and Peter Vojtas @Charles University Prague

http://ir.ii.uam.es/rue2012/papers/rue2012-peska.pdf

 

Recommender Systems Evaluation: A 3D Benchmark

Alan Said @alansaid @TU Berlin

Domonkos Tikk and Klara Stumpf @Gravity Berlin

Yue Shi and Martha Larson @TU-Delft

Paolo Cremonesi @Politecnico di Milano

http://ir.ii.uam.es/rue2012/papers/rue2012-said.pdf

 

On the use of Weighted Mean Absolute Error in Recommender Systems

S. Cleger-Tamayo @Universidad de Holguín

J.M. Fernández-Luna & J.F. Huete @UGR Universidad de Granada, Spain

http://ir.ii.uam.es/rue2012/papers/rue2012-cleger-tamayo.pdf

 

How Similar is Rating Similarity to Content Similarity?

Osman Ba¸skaya and Tevfik Aytekin @Bahçe¸sehir University

http://ir.ii.uam.es/rue2012/papers/rue2012-baskaya.pdf

 

Modeling Difficulty in Recommender Systems

Benjamin Kille and Sahin Albayrak @DAI-Lab Technische Universit at Berlin

http://ir.ii.uam.es/rue2012/papers/rue2012-kille.pdf

 

Consumer Science and Product Development at Netflix

Rochelle King @Netlix

Matt Marenghi @mattmarenghi @Netflix

http://www.slideshare.net/mattmarenghi/consumer-science-and-product-development-at-netflix

 

Aggregating Content and Network Information to Curate Twitter User Lists

Derek Greene, Gavin Sheridan, Barry Smyth @University College Dublin

Padraig Cunningham @Storyful

http://arxiv.org/pdf/1206.1728v2.pdf

 

Online Dating Recommender Systems

Dr. Jérôme Kunegis @kunegis Gerd Gröner @gerdgroener & Thomas Gottron @tgottron

WeST – Institute for Web Science and Technologies,  @University of Koblenz- Landau

http://userpages.uni-koblenz.de/~kunegis/paper/kunegis-online-dating-recommender-systems-the-split-complex-number-approach.pdf

 

Toward a New Protocol to Evaluate Recommender Systems

Frank Meyer, Françoise Fessant, and Fabrice Clérot @OrangeLabs

Eric Gaussier @University of Grenoble

http://ir.ii.uam.es/rue2012/papers/rue2012-meyer.pdf

 

Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

Alejandro Bellogín @abellogin @uam_madrid

javier Parapar @jparapar @University of Coruña

http://ir.ii.uam.es/~alejandro/2012/recsys-poster.pdf

 

Rate it Again: Increasing Recommendation Accuracy by User re-Rating

Xavier Amatriain @xamat @telefonicaid (now @Netflix)

Josep M. Pujol and Nava Tintarev @telefonicaid

http://homepages.abdn.ac.uk/csc284/pages/amatriain_RecSys09.pdf

 

User Rating to Profile Health Demo at RecSys2012

Dr. Neal Lathia @neal_lathia  @Cambridge_Uni

http://www.slideshare.net/neal.lathia/recsys-2012-demo-long

 

Recommendations and Discovery at StumbleUpon

Sumanth Kolar @_5K @StumbleUpon

http://www.slideshare.net/sumanthkolar/recsys-2012-sumanth-14260370

 

Hadoop and Recommendations @LinkedIn

Abhishek Gupta @abhishek85gupta  and Adil Aijaz @adilaijaz @LinkedIn

http://slidesha.re/PvlCOL

 

Predicting Personality with Twitter

Daniele Quercia @danielequercia @Cambridge_Uni

David Stillwell @david_stillwell @University of Nottingham

Michal Kosinskiy @michalkosinski @Cambridge_Uni

Jon Crowcroftx  @tforcworc @Cambridge_Uni

http://www.cl.cam.ac.uk/~dq209/publications/quercia11twitter.pdf

 

The Personality of Popular Facebook Users

Daniele Quercia @danielequercia @Cambridge_Uni

Renaud Lambiottez  @RenaudLambiotte @Imperial College

David Stillwell @david_stillwell @University of Nottingham

Michal Kosinskiy @michalkosinski @Cambridge_Uni

Jon Crowcroftx  @tforcworc @Cambridge_Uni

http://www.cl.cam.ac.uk/~dq209/publications/quercia12personality.pdf

 

Mendeley’s Data and Perspectives on Data Challenges

Kris Jack @_krisjack @Mendeley

http://www.slideshare.net/KrisJack/rec-sys12mendeleydatachallenges

 

What is so special about music?

Paul Lamere @plamere @echonest

http://musicmachinery.com/2011/10/23/what-is-so-special-about-music/

 

Dating Sites and the Split-complex Numbers

Dr. Jérôme Kunegis @kunegis @University of Koblenz- Landau

http://networkscience.wordpress.com/2011/08/09/dating-sites-and-the-split-complex-numbers/

 

Explaining the user experience of recommender systems

Bart Knijnenburg @usabart @UCIrvine

Z. Gantner University of Hildesheim

A.  Soncu European @Microsoft Innovation Center

C. Newell @BBC Research & Development

http://www.springerlink.com/content/d53h33763855w3pk/fulltext.pdf

 

Effects of Online Recommendations on Consumers’ Willingness to Pay

Gediminas Adomavicius & Shawn Curley @University of Minnesota

Jesse Bockstedt @University of Arizona

Jingjing Zhang @Indiana University

http://recex.ist.tugraz.at/RecSysWorkshop2012/04_Adomavicius_et_al.pdf

 

Decision-Making in Recommender Systems: The Role of User’s Goals and Bounded Resources

Paolo Cremonesi & Franca Garzotto @Politecnico di Milano

Antonio Donatacci & Roberto Turrin @Moviri

http://recex.ist.tugraz.at/RecSysWorkshop2012/03_Cremonesi_et_al.pdf

 

Eliciting Stakeholder Preferences for Requirements Prioritization

Alexander Felfernig, Gerald Ninaus & Florian Reinfrank @Graz University

http://recex.ist.tugraz.at/RecSysWorkshop2012/05_Felfernig_et_al.pdf

 

Recommendation systems in the scope of opinion formation: a model

Marcel Blattner @University of Applied Sciences FFHSRegensdorf

Matus Medo  @University of Fribourg

http://recex.ist.tugraz.at/RecSysWorkshop2012/06_Blattner_et_al.pdf

 

The Effect of Sensitivity Analysis on the Usage of Recommender Systems

Martina Maida, Konradin Maier & Nikolaus Obwegeser @Vienna University of Economics and Business

http://recex.ist.tugraz.at/RecSysWorkshop2012/07_Maida_et_al.pdf

 

Recommending Personalized Query Revisions

Henry Blanco & Francesco Ricci @Free University of Bolzano

Derek Bridge @ University College Cork

http://recex.ist.tugraz.at/RecSysWorkshop2012/02_Blanco_et_al.pdf

 

 

Presentations posted and tagged as #RecSys2012

http://www.slideshare.net/tag/recsys2012

http://www.slideshare.net/search/slideshow?searchfrom=header&q=%23recsys2012

 

The Recsys2012 Challenges

 

The Official Recommender Systems Challenge 2012 Webpage

http://2012.recsyschallenge.com/schedule/

 

The Limerick 2012 Challenge

Winner Dr. Neal Lathia @neal_lathia @Cambridge_Uni

http://acmrecsys.wordpress.com/2011/10/26/the-recsys-2012-limerick-challenge/

 

The Official Stuff

Proceedings of the 2012 ACM Conference on Recommender Systems 2012-09-09

Pádraig Cunningham, Neil Hurley, Ido Guy, Sarabjot, Singh Anand @ACM

http://hcibib.org/RecSys12

 

 

 

 

One comment:

  • Yet Another RecSys 2012 Summary « alan said on September 24, 2012 at 1:11 pm

    […] Data Science London –  Recsys 2012 – (Probably) Most commented and tweeted papers & presentations ACM Recommender Sys… […]

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