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 S. N. 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 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 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

last updated on Tue May 21 02:25:30 2013 by the dblp team
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