Table of Contents

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