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ORCIDRecSys 2012: Dublin, Ireland
- Padraig Cunningham, Neil J. Hurley, Ido Guy, Sarabjot Singh Anand:
Sixth ACM Conference on Recommender Systems, RecSys '12, Dublin, Ireland, September 9-13, 2012. ACM 2012, ISBN 978-1-4503-1270-7
Keynote address
- Ron Kohavi:
Online controlled experiments: introduction, learnings, and humbling statistics. 1-2
Tutorials
- Bart P. Knijnenburg:
Conducting user experiments in recommender systems. 3-4 - Maria Augusta Silveira Netto Nunes
, Rong Hu:
Personality-based recommender systems: an overview. 5-6 - Xavier Amatriain:
Building industrial-scale real-world recommender systems. 7-8 - Alan Said, Domonkos Tikk, Andreas Hotho:
The challenge of recommender systems challenges. 9-10
Multi-objective recommendation and human factors
- Mario Rodríguez, Christian Posse, Ethan Zhang:
Multiple objective optimization in recommender systems. 11-18 - Marco Túlio Ribeiro, Anísio Lacerda, Adriano Veloso, Nivio Ziviani:
Pareto-efficient hybridization for multi-objective recommender systems. 19-26 - Paolo Cremonesi, Franca Garzotto, Roberto Turrin:
User effort vs. accuracy in rating-based elicitation. 27-34 - Svetlin Bostandjiev, John O'Donovan, Tobias Höllerer:
TasteWeights: a visual interactive hybrid recommender system. 35-42
Social recommendation
- Bart P. Knijnenburg, Svetlin Bostandjiev, John O'Donovan, Alfred Kobsa:
Inspectability and control in social recommenders. 43-50 - Xiaolan Sha, Daniele Quercia, Pietro Michiardi, Matteo Dell'Amico:
Spotting trends: the wisdom of the few. 51-58 - Ernesto Diaz-Aviles, Lucas Drumond, Lars Schmidt-Thieme, Wolfgang Nejdl:
Real-time top-n recommendation in social streams. 59-66 - Xiwang Yang, Harald Steck, Yang Guo, Yong Liu:
On top-k recommendation using social networks. 67-74
Implicit feedback and user preference
- Omar Moling, Linas Baltrunas, Francesco Ricci:
Optimal radio channel recommendations with explicit and implicit feedback. 75-82 - Gábor Takács, Domonkos Tikk:
Alternating least squares for personalized ranking. 83-90 - Diyi Yang, Tianqi Chen, Weinan Zhang, Qiuxia Lu, Yong Yu:
Local implicit feedback mining for music recommendation. 91-98 - Daniel Kluver, Tien T. Nguyen, Michael D. Ekstrand, Shilad Sen, John Riedl:
How many bits per rating? 99-106
Contextual and semantically aware recommendation
- Sven Strickroth, Niels Pinkwart:
High quality recommendations for small communities: the case of a regional parent network. 107-114 - Asher Levi, Osnat Mokryn, Christophe Diot, Nina Taft:
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. 115-122 - Sindhu Raghavan, Suriya Gunasekar, Joydeep Ghosh:
Review quality aware collaborative filtering. 123-130 - Negar Hariri, Bamshad Mobasher, Robin D. Burke:
Context-aware music recommendation based on latenttopic sequential patterns. 131-138
Top-N recommendation
- Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha A. Larson, Nuria Oliver, Alan Hanjalic:
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. 139-146 - Bruno Pradel, Nicolas Usunier, Patrick Gallinari:
Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics. 147-154 - Xia Ning, George Karypis:
Sparse linear methods with side information for top-n recommendations. 155-162 - Sebastian Schelter, Christoph Boden, Volker Markl:
Scalable similarity-based neighborhood methods with MapReduce. 163-170
Emerging themes
- Bruno Antunes, Joel Cordeiro, Paulo Gomes:
An approach to context-based recommendation in software development. 171-178 - Weinan Zhang, Li Tian, Xinruo Sun, Haofen Wang, Yong Yu:
A semantic approach to recommending text advertisements for images. 179-186 - Diego Sáez-Trumper, Daniele Quercia, Jon Crowcroft:
Ads and the city: considering geographic distance goes a long way. 187-194 - Udi Weinsberg, Smriti Bhagat, Stratis Ioannidis, Nina Taft:
BlurMe: inferring and obfuscating user gender based on ratings. 195-202
Industry session 1
- Ralf Herbrich:
Distributed, real-time bayesian learning in online services. 203-204 - Ronny Lempel:
Recommendation challenges in web media settings. 205-206
Industry session 2
- Paul Lamere:
I've got 10 million songs in my pocket: now what? 207-208
Short papers
- Michal Aharon, Amit Kagian, Ronny Lempel, Yehuda Koren:
Dynamic personalized recommendation of comment-eliciting stories. 209-212 - Alejandro Bellogín, Javier Parapar:
Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. 213-216 - Dirk G. F. M. Bollen, Mark P. Graus, Martijn C. Willemsen:
Remembering the stars?: effect of time on preference retrieval from memory. 217-220 - Abir De, Maunendra Sankar Desarkar, Niloy Ganguly, Pabitra Mitra:
Local learning of item dissimilarity using content and link structure. 221-224 - Toon De Pessemier, Simon Dooms, Luc Martens:
Design and evaluation of a group recommender system. 225-228 - Ernesto Diaz-Aviles, Mihai Georgescu, Wolfgang Nejdl:
Swarming to rank for recommender systems. 229-232 - Michael D. Ekstrand, John Riedl:
When recommenders fail: predicting recommender failure for algorithm selection and combination. 233-236 - Yu-Jia Huang, Evan Wei Xiang, Rong Pan:
Constrained collective matrix factorization. 237-240 - Yichen Jiang, Aixia Jia, Yansong Feng, Dongyan Zhao:
Recommending academic papers via users' reading purposes. 241-244 - Qi Liu, Biao Xiang, Enhong Chen, Yong Ge, Hui Xiong, Tengfei Bao, Yi Zheng:
Influential seed items recommendation. 245-248 - Marcelo G. Manzato:
Discovering latent factors from movies genres for enhanced recommendation. 249-252 - Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito:
Exploiting the web of data in model-based recommender systems. 253-256 - Shankar Prawesh, Balaji Padmanabhan:
Probabilistic news recommender systems with feedback. 257-260 - Tim Salimans, Ulrich Paquet, Thore Graepel:
Collaborative learning of preference rankings. 261-264 - Shanchan Wu, Leanna Gong, William Rand, Louiqa Raschid:
Making recommendations in a microblog to improve the impact of a focal user. 265-268 - Markus Zanker:
The influence of knowledgeable explanations on users' perception of a recommender system. 269-272
Industry short papers
- Mohammad Shafkat Amin, Baoshi Yan, Sripad Sriram, Anmol Bhasin, Christian Posse:
Social referral: leveraging network connections to deliver recommendations. 273-276 - Thiago Belluf, Leopoldo Xavier, Ricardo Giglio:
Case study on the business value impact of personalized recommendations on a large online retailer. 277-280 - Noam Koenigstein, Nir Nice, Ulrich Paquet, Nir Schleyen:
The Xbox recommender system. 281-284 - Qiwen Liu, Tianjian Chen, Jing Cai, Dianhai Yu:
Enlister: baidu's recommender system for the biggest chinese Q&A website. 285-288 - Barry Smyth, Maurice Coyle, Peter Briggs:
HeyStaks: a real-world deployment of social search. 289-292
Demonstrations
- Igor Brigadir, Derek Greene, Padraig Cunningham:
A system for twitter user list curation. 293-294 - Sidharth Chhabra, Paul Resnick:
CubeThat: news article recommender. 295-296 - Ruihai Dong, Markus Schaal, Michael P. O'Mahony, Kevin McCarthy, Barry Smyth:
The demonstration of the reviewer's assistant. 297-298 - Abigail S. Gertner, Beth Lavender, James Winston:
Recommenders for the enterprise: event, contact, and group. 299-300 - James Griffin:
Integrated content marketing. 301-302 - Neal Lathia:
Using ratings to profile your health. 303-304 - Asher Levi, Osnat Mokryn, Christophe Diot, Nina Taft:
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system demo. 305-306 - Owen Phelan, Kevin McCarthy, Barry Smyth:
Yokie: explorations in curated real-time search & discovery using twitter. 307-308 - Mark Sheehan, Young Park:
pGPA: a personalized grade prediction tool to aid student success. 309-310 - Max Sklar, Blake Shaw, Andrew Hogue:
Recommending interesting events in real-time with foursquare check-ins. 311-312
Doctoral symposium
- Benjamin Heitmann:
An open framework for multi-source, cross-domain personalisation with semantic interest graphs. 313-316 - Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Exploiting the characteristics of matrix factorization for active learning in recommender systems. 317-320 - Tomás Kramár, Mária Bieliková:
Dynamically selecting an appropriate context type for personalisation. 321-324 - Nikolas Landia:
Utilising document content for tag recommendation in folksonomies. 325-328 - Gerald Ninaus:
Using group recommendation heuristics for the prioritization of requirements. 329-332 - Denis Parra:
Beyond lists: studying the effect of different recommendation visualizations. 333-336 - Simon Wakeling:
The user-centered design of a recommender system for a universal library catalogue. 337-340 - Dusan Zeleník, Mária Bieliková:
Reducing the sparsity of contextual information for recommender systems. 341-344
Workshop outlines
- Bamshad Mobasher, Dietmar Jannach, Werner Geyer, Andreas Hotho:
4th ACM RecSys workshop on recommender systems and the social web. 345-346 - Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Francesco Ricci, Giovanni Semeraro, Martijn C. Willemsen:
RecSys'12 workshop on human decision making in recommender systems. 347-348 - Gediminas Adomavicius, Linas Baltrunas, Ernesto William De Luca, Tim Hussein, Alexander Tuzhilin:
4th workshop on context-aware recommender systems (CARS 2012). 349-350 - Xavier Amatriain, Pablo Castells, Arjen P. de Vries, Christian Posse:
Workshop on recommendation utility evaluation: beyond RMSE - RUE 2012. 351-352 - Nikos Manouselis, Alan Said, Domonkos Tikk, Jannis Hermanns, Benjamin Kille, Hendrik Drachsler, Katrien Verbert, Kris Jack:
Recommender systems challenge 2012. 353-354 - Nava Tintarev, Rong Hu, Pearl Pu:
RecSys'12 workshop on interfaces for recommender systems (InterfaceRS'12). 355-356 - Bernd Ludwig, Francesco Ricci, Zerrin Yumak:
1st workshop on recommendation technologies for lifestyle change 2012. 357-358 - Henriette Cramer, Karen Church, Neal Lathia, Daniele Quercia:
Personalizing the local mobile experience: workshop at RecSys 2012. 359-360
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