Table of Contents

• Recommender systems at the long tailNeel Sundaresan. 1-6 [doi]
• Music recommendation and discovery revisitedÒscar Celma, Paul Lamere. 7-8 [doi]
• Robustness of recommender systemsNeil J. Hurley. 9-10 [doi]
• Recommendations as a conversation with the userDaniel Tunkelang. 11-12 [doi]
• Generalizing matrix factorization through flexible regression priorsLiang Zhang, Deepak Agarwal, Bee-Chung Chen. 13-20 [doi]
• Modeling item selection and relevance for accurate recommendations: a bayesian approachNicola Barbieri, Gianni Costa, Giuseppe Manco, Riccardo Ortale. 21-28 [doi]
• Shared collaborative filteringYu Zhao 0002, Xinping Feng, JianQiang Li, Bo Liu. 29-36 [doi]
• Wisdom of the better few: cold start recommendation via representative based rating elicitationNathan Nan Liu, Xiangrui Meng, Chao Liu, Qiang Yang. 37-44 [doi]
• Personalized PageRank vectors for tag recommendations: inside FolkRankHeung-Nam Kim, Abdulmotaleb El-Saddik. 45-52 [doi]
• A generalized stochastic block model for recommendation in social rating networksMohsen Jamali, Tianle Huang, Martin Ester. 53-60 [doi]
• Product recommendation and rating prediction based on multi-modal social networksPanagiotis Symeonidis, Eleftherios Tiakas, Yannis Manolopoulos. 61-68 [doi]
• Distributed rating prediction in user generated content streamsSibren Isaacman, Stratis Ioannidis, Augustin Chaintreau, Margaret Martonosi. 69-76 [doi]
• Multi-criteria service recommendation based on user criteria preferencesLiwei Liu, Nikolay Mehandjiev, Dong-Ling Xu. 77-84 [doi]
• The effect of context-aware recommendations on customer purchasing behavior and trustMichele Gorgoglione, Umberto Panniello, Alexander Tuzhilin. 85-92 [doi]
• Random walk based entity ranking on graph for multidimensional recommendationSangKeun Lee, Sang-il Song, Minsuk Kahng, Dongjoo Lee, Sang-goo Lee. 93-100 [doi]
• Group recommendation using feature space representing behavioral tendency and power balance among membersShunichi Seko, Takashi Yagi, Manabu Motegi, Shin-yo Muto. 101-108 [doi]
• Rank and relevance in novelty and diversity metrics for recommender systemsSaul Vargas, Pablo Castells. 109-116 [doi]
• OrdRec: an ordinal model for predicting personalized item rating distributionsYehuda Koren, Joe Sill. 117-124 [doi]
• Item popularity and recommendation accuracyHarald Steck. 125-132 [doi]
• Rethinking the recommender research ecosystem: reproducibility, openness, and LensKitMichael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, John Riedl. 133-140 [doi]
• Each to his own: how different users call for different interaction methods in recommender systemsBart P. Knijnenburg, Niels J. M. Reijmer, Martijn C. Willemsen. 141-148 [doi]
• Rating: how difficult is it?E. Isaac Sparling, Shilad Sen. 149-156 [doi]
• A user-centric evaluation framework for recommender systemsPearl Pu, Li Chen, Rong Hu. 157-164 [doi]
• Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomyNoam Koenigstein, Gideon Dror, Yehuda Koren. 165-172 [doi]
• CrimeWalker: a recommendation model for suspect investigationMohammad A. Tayebi, Mohsen Jamali, Martin Ester, Uwe Glässer, Richard Frank. 173-180 [doi]
• Personalized activity streams: sifting through the "river of news"Ido Guy, Inbal Ronen, Ariel Raviv. 181-188 [doi]
• Social link recommendation by learning hidden topicsMasoud Makrehchi. 189-196 [doi]
• Enhancing collaborative filtering systems with personality informationRong Hu, Pearl Pu. 197-204 [doi]
• A market-based approach to address the new item problemSarabjot Singh Anand, Nathan Griffiths. 205-212 [doi]
• My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendationKibeom Lee, Kyogu Lee. 213-220 [doi]
• Multi-value probabilistic matrix factorization for IP-TV recommendationsYu Xin, Harald Steck. 221-228 [doi]
• A probabilistic definition of item similarityOliver Jojic, Manu Shukla, Niranjan Bhosarekar. 229-236 [doi]
• The "top N" news recommender: count distortion and manipulation resistanceShankar Prawesh, Balaji Padmanabhan. 237-244 [doi]
• Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendationQuan Yuan, Li Chen, Shiwan Zhao. 245-252 [doi]
• Recommending music for places of interest in a mobile travel guideMatthias Braunhofer, Marius Kaminskas, Francesco Ricci. 253-256 [doi]
• Adaptive social similarities for recommender systemsLe Yu, Rong Pan, Zhangfeng Li. 257-260 [doi]
• Content-boosted matrix factorization for recommender systems: experiments with recipe recommendationPeter Forbes, Mu Zhu. 261-264 [doi]
• Interactive multi-party critiquing for group recommendationFrancesca Guzzi, Francesco Ricci, Robin D. Burke. 265-268 [doi]
• A model for proactivity in mobile, context-aware recommender systemsWolfgang Woerndl, Johannes Huebner, Roland Bader, Daniel Gallego-Vico. 273-276 [doi]
• Effective event discovery: using location and social information for scoping event recommendationsElizabeth M. Daly, Werner Geyer. 277-280 [doi]
• Collaborative filtering with collective trainingYong Ge, Hui Xiong, Alexander Tuzhilin, Qi Liu. 281-284 [doi]
• Using Wikipedia to boost collaborative filtering techniquesGilad Katz, Nir Ofek, Bracha Shapira, Lior Rokach, Guy Shani. 285-288 [doi]
• Semi-SAD: applying semi-supervised learning to shilling attack detectionZhiang Wu, Jie Cao, Bo Mao, Youquan Wang. 289-292 [doi]
• Leveraging the linkedin social network data for extracting content-based user profilesPasquale Lops, Marco de Gemmis, Giovanni Semeraro, Fedelucio Narducci, Cataldo Musto. 293-296 [doi]
• Applications of the conjugate gradient method for implicit feedback collaborative filteringGábor Takács, István Pilászy, Domonkos Tikk. 297-300 [doi]
• Matrix factorization techniques for context aware recommendationLinas Baltrunas, Bernd Ludwig, Francesco Ricci. 301-304 [doi]
• MyMediaLite: a free recommender system libraryZeno Gantner, Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme. 305-308 [doi]
• Towards a more realistic evaluation: testing the ability to predict future tastes of matrix factorization-based recommendersPedro G. Campos, Fernando Díez, Manuel A. Sánchez-Montañés. 309-312 [doi]
• Collaborative temporal order modelingAlexandros Karatzoglou. 313-316 [doi]
• LOGO: a long-short user interest integration in personalized news recommendationLei Li, Li Zheng, Tao Li. 317-320 [doi]
• A pragmatic procedure to support the user-centric evaluation of recommender systemsBart P. Knijnenburg, Martijn C. Willemsen, Alfred Kobsa. 321-324 [doi]
• Machine learned job recommendationIoannis K. Paparrizos, Berkant Barla Cambazoglu, Aristides Gionis. 325-328 [doi]
• Utilizing related products for post-purchase recommendation in e-commerceJian Wang, Badrul Sarwar, Neel Sundaresan. 329-332 [doi]
• Precision-oriented evaluation of recommender systems: an algorithmic comparisonAlejandro Bellogín, Pablo Castells, Iván Cantador. 333-336 [doi]
• Power to the people: exploring neighbourhood formations in social recommender systemSteven Bourke, Kevin McCarthy, Barry Smyth. 337-340 [doi]
• Stochastic matching and collaborative filtering to recommend people to peopleLuiz Augusto Sangoi Pizzato, Cameron Silvestrini. 341-344 [doi]
• Recommendations in social media for brand monitoringShanchan Wu, William Rand, Louiqa Raschid. 345-348 [doi]
• LensKit: a modular recommender frameworkMichael D. Ekstrand, Michael Ludwig, Jack Kolb, John Riedl. 349-350 [doi]
• myMicSound: an online sound-based microphone recommendation systemAndrew T. Sabin, Chun Liang Chan. 351-352 [doi]
• Recommenders benchmark frameworkAviram Dayan, Guy Katz, Naseem Biasdi, Lior Rokach, Bracha Shapira, Aykan Aydin, Roland Schwaiger, Radmila Fishel. 353-354 [doi]
• Using canonical correlation analysis for generalized sentiment analysis, product recommendation and searchSiamak Faridani. 355-358 [doi]
• Design guidelines for mobile group recommender systems to handle inaccurate or missing location dataMarkus Tschersich. 359-362 [doi]
• Interface and interaction design for group and social recommender systemsYu Chen. 363-366 [doi]
• Intelligent web usage clustering based recommender systemShafiq Alam. 367-370 [doi]
• Predicting performance in recommender systemsAlejandro Bellogín. 371-374 [doi]
• Anchoring effects of recommender systemsJingjing Zhang. 375-378 [doi]
• 3rd workshop on context-aware recommender systems (CARS 2011)Gediminas Adomavicius, Linas Baltrunas, Tim Hussein, Francesco Ricci, Alexander Tuzhilin. 379-380 [doi]
• WOMRAD: 2nd workshop on music recommendation and discoveryAmelie Anglade, Òscar Celma, Ben Fields, Paul Lamere, Brian McFee. 381-382 [doi]
• 3rd workshop on recommender systems and the social webJill Freyne, Sarabjot Singh Anand, Ido Guy, Andreas Hotho. 383-384 [doi]
• Challenge on context-aware movie recommendation: CAMRa2011Alan Said, Shlomo Berkovsky, Ernesto William De Luca, Jannis Hermanns. 385-386 [doi]
• Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)Iván Cantador, Peter Brusilovsky, Tsvi Kuflik. 387-388 [doi]
• RecSys'11 workshop on human decision making in recommender systemsAlexander Felfernig, Li Chen, Monika Mandl. 389-390 [doi]
• Recsys'11 workshop outline PeMA 2011: personalization in mobile applicationsNeal Lathia, Daniele Quercia, Licia Capra, Jon Crowcroft. 391-392 [doi]
• Workshop on novelty and diversity in recommender systems - DiveRS 2011Pablo Castells, Jun Wang 0012, Rubén Lara, Dell Zhang. 393-394 [doi]
• UCERSTI 2: second workshop on user-centric evaluation of recommender systems and their interfacesMartijn C. Willemsen, Dirk G. F. M. Bollen, Michael D. Ekstrand. 395-396 [doi]