Table of Contents

• LinkedIn skills: large-scale topic extraction and inferenceMathieu Bastian, Matthew Hayes, William Vaughan, Sam Shah, Peter Skomoroch, Hyung Jin Kim, Sal Uryasev, Christopher Lloyd. 1-8 [doi]
• Automating readers' advisory to make book recommendations for K-12 readersMaria Soledad Pera, Yiu-Kai Ng. 9-16 [doi]
• Exploiting sentiment homophily for link predictionGuangchao Yuan, Pradeep K. Murukannaiah, Zhe Zhang, Munindar P. Singh. 17-24 [doi]
• A robust model for paper reviewer assignmentXiang Liu, Torsten Suel, Nasir D. Memon. 25-32 [doi]
• Factored MDPs for detecting topics of user sessionsMaryam Tavakol, Ulf Brefeld. 33-40 [doi]
• Context adaptation in interactive recommender systemsNegar Hariri, Bamshad Mobasher, Robin D. Burke. 41-48 [doi]
• Question recommendation with constraints for massive open online coursesDiyi Yang, David Adamson, Carolyn Penstein Rosé. 49-56 [doi]
• Attacking item-based recommender systems with power itemsCarlos E. Seminario, David C. Wilson. 57-64 [doi]
• Recommending with an agenda: active learning of private attributes using matrix factorizationSmriti Bhagat, Udi Weinsberg, Stratis Ioannidis, Nina Taft. 65-72 [doi]
• Ensemble contextual bandits for personalized recommendationLiang Tang, Yexi Jiang, Lei Li, Tao Li. 73-80 [doi]
• Cold-start news recommendation with domain-dependent browse graphMichele Trevisiol, Luca Maria Aiello, Rossano Schifanella, Alejandro Jaimes. 81-88 [doi]
• Item cold-start recommendations: learning local collective embeddingsMartin Saveski, Amin Mantrach. 89-96 [doi]
• Improving the discriminative power of inferred content information using segmented virtual profileHaishan Liu, Anuj Goyal, Trevor Walker, Anmol Bhasin. 97-104 [doi]
• Ratings meet reviews, a combined approach to recommendGuang Ling, Michael R. Lyu, Irwin King. 105-112 [doi]
• Beyond clicks: dwell time for personalizationXing Yi, Liangjie Hong, ErHeng Zhong, Nanthan Nan Liu, Suju Rajan. 113-120 [doi]
• Evaluating recommender behavior for new usersDaniel Kluver, Joseph A. Konstan. 121-128 [doi]
• Comparative recommender system evaluation: benchmarking recommendation frameworksAlan Said, Alejandro Bellogín. 129-136 [doi]
• A methodology for learning, analyzing, and mitigating social influence bias in recommender systemsSanjay Krishnan, Jay Patel, Michael J. Franklin, Ken Goldberg. 137-144 [doi]
• Improving sales diversity by recommending users to itemsSaul Vargas, Pablo Castells. 145-152 [doi]
• On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systemsPanagiotis Adamopoulos, Alexander Tuzhilin. 153-160 [doi]
• User perception of differences in recommender algorithmsMichael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, Joseph A. Konstan. 161-168 [doi]
• Offline and online evaluation of news recommender systems at swissinfo.chFlorent Garcin, Boi Faltings, Olivier Donatsch, Ayar Alazzawi, Christophe Bruttin, Amr Huber. 169-176 [doi]
• Unifying nearest neighbors collaborative filteringKoen Verstrepen, Bart Goethals. 177-184 [doi]
• Recommending user generated item listsYidan Liu, Min Xie 0002, Laks V. S. Lakshmanan. 185-192 [doi]
• Question recommendation for collaborative question answering systems with RankSLDAJose San Pedro, Alexandros Karatzoglou. 193-200 [doi]
• Bayesian binomial mixture model for collaborative prediction with non-random missing dataYong-Deok Kim, Seungjin Choi. 201-208 [doi]
• Coverage, redundancy and size-awareness in genre diversity for recommender systemsSaúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, Pablo Castells. 209-216 [doi]
• Towards a dynamic top-N recommendation frameworkXin Liu, Karl Aberer. 217-224 [doi]
• Explore-exploit in top-N recommender systems via Gaussian processesHastagiri P. Vanchinathan, Isidor Nikolic, Fabio De Bona, Andreas Krause. 225-232 [doi]
• A parameter-free algorithm for an optimized tag recommendation list sizeModou Gueye, Talel Abdessalem, Hubert Naacke. 233-240 [doi]
• GASGD: stochastic gradient descent for distributed asynchronous matrix completion via graph partitioningFabio Petroni, Leonardo Querzoni. 241-248 [doi]
• A framework for matrix factorization based on general distributionsJosef Bauer, Alexandros Nanopoulos. 249-256 [doi]
• Speeding up the Xbox recommender system using a euclidean transformation for inner-product spacesYoram Bachrach, Yehuda Finkelstein, Ran Gilad-Bachrach, Liran Katzir, Noam Koenigstein, Nir Nice, Ulrich Paquet. 257-264 [doi]
• Gradient boosting factorization machinesChen Cheng, Fen Xia, Tong Zhang, Irwin King, Michael R. Lyu. 265-272 [doi]
• Exploiting temporal influence in online recommendationRóbert Pálovics, András A. Benczúr, Levente Kocsis, Tamás Kiss, Erzsébet Frigó. 273-280 [doi]
• 'Free lunch' enhancement for collaborative filtering with factorization machinesBabak Loni, Alan Said, Martha Larson, Alan Hanjalic. 281-284 [doi]
• An analysis of users' propensity toward diversity in recommendationsTommaso Di Noia, Vito Claudio Ostuni, Jessica Rosati, Paolo Tomeo, Eugenio Di Sciascio. 285-288 [doi]
• Clinical online recommendation with subgroup rank feedbackYanan Sui, Joel Burdick. 289-292 [doi]
• Convex AUC optimization for top-N recommendation with implicit feedbackFabio Aiolli. 293-296 [doi]
• Cross-domain recommendations without overlapping data: myth or reality?Paolo Cremonesi, Massimo Quadrana. 297-300 [doi]
• CSLIM: contextual SLIM recommendation algorithmsYong Zheng, Bamshad Mobasher, Robin D. Burke. 301-304 [doi]
• Dynamics of human trust in recommender systemsJason L. Harman, John O'Donovan, Tarek F. Abdelzaher, Cleotilde Gonzalez. 305-308 [doi]
• Eliciting the users' unknown preferencesJulia Neidhardt, Rainer Schuster, Leonhard Seyfang, Hannes Werthner. 309-312 [doi]
• Emphasize, don't filter!: displaying recommendations in Twitter timelinesWesley Waldner, Julita Vassileva. 313-316 [doi]
• Implicit vs. explicit trust in social matrix factorizationSoude Fazeli, Babak Loni, Alejandro Bellogin, Hendrik Drachsler, Peter B. Sloep. 317-320 [doi]
• Modeling the dynamics of user preferences in coupled tensor factorizationDimitrios Rafailidis, Alexandros Nanopoulos. 321-324 [doi]
• Multi-criteria journey aware housing recommender systemElizabeth M. Daly, Adi Botea, Akihiro Kishimoto, Radu Marinescu. 325-328 [doi]
• PERSPeCT: collaborative filtering for tailored health communicationsRoy J. Adams, Rajani S. Sadasivam, Kavitha Balakrishnan, Rebecca L. Kinney, Thomas K. Houston, Benjamin M. Marlin. 329-332 [doi]
• Preference elicitation for narrowing the recommended list for groupsLihi Naamani Dery, Meir Kalech, Lior Rokach, Bracha Shapira. 333-336 [doi]
• Recommendation-based modeling support for data mining processesDietmar Jannach, Simon Fischer. 337-340 [doi]
• Scalable audience targeted models for brand advertising on social networksKunpeng Zhang, Aris M. Ouksel, Shaokun Fan, Hengchang Liu. 341-344 [doi]
• Social collaborative filtering for cold-start recommendationsSuvash Sedhain, Scott Sanner, Darius Braziunas, Lexing Xie, Jordan Christensen. 345-348 [doi]
• Switching hybrid for cold-starting context-aware recommender systemsMatthias Braunhofer, Victor Codina, Francesco Ricci. 349-352 [doi]
• Using graded implicit feedback for bayesian personalized rankingLukas Lerche, Dietmar Jannach. 353-356 [doi]
• Inferring user interests in the Twitter social networkParantapa Bhattacharya, Muhammad Bilal Zafar, Niloy Ganguly, Saptarshi Ghosh, Krishna P. Gummadi. 357-360 [doi]
• Aspect-based opinion mining and recommendationsystem for restaurant reviewsVaishak Suresh, Syeda Roohi, Magdalini Eirinaki. 361-362 [doi]
• Configuring and monitoring recommender system as a serviceDavid Ben-Shimon, Alexander Tsikinovsky, Michael Friedmann, Johannes Hörle. 363-364 [doi]
• Content ordering based on commuting patternsTravis Gingerich, Omar Alonso. 365-366 [doi]
• Cosibon: an E-commerce like platform enabling bricks-and-mortar stores to use sophisticated product recommender systemsThorben Keller, Matthias Raffelsieper. 367-368 [doi]
• Focal: a personalized mobile news readerFlorent Garcin, Frederik Galle, Boi Faltings. 369-370 [doi]
• Rival: a toolkit to foster reproducibility in recommender system evaluationAlan Said, Alejandro Bellogín. 371-372 [doi]
• System U: automatically deriving personality traits from social media for people recommendationHernan Badenes, Mateo N. Bengualid, Jilin Chen, Liang Gou, Eben M. Haber, Jalal Mahmud, Jeffrey W. Nichols, Aditya Pal, Jerald Schoudt, Barton A. Smith, Ying Xuan, Huahai Yang, Michelle X. Zhou. 373-374 [doi]
• Tell me where to go and what to do next, but do not bother meHongwei Liu, Gang Wu, Guoren Wang. 375-376 [doi]
• WrapRec: an easy extension of recommender system librariesBabak Loni, Alan Said. 377-378 [doi]
• Workshop on new trends in content-based recommender systems: (CBRecSys 2014)Toine Bogers, Marijn Koolen, Iván Cantador. 379-380 [doi]
• Overview of ACM RecSys CrowdRec 2014 workshop: crowdsourcing and human computation for recommender systemsMartha Larson, Paolo Cremonesi, Alexandros Karatzoglou. 381-382 [doi]
• RecSys'14 joint workshop on interfaces and human decision making for recommender systemsNava Tintarev, John O'Donovan, Peter Brusilovsky, Alexander Felfernig, Giovanni Semeraro, Pasquale Lops. 383-384 [doi]
• Second workshop on large-scale recommender systems: research and best practice (LSRS 2014)Tao Ye, Danny Bickson, Qiang Yan. 385-386 [doi]
• Recommender systems challenge 2014Alan Said, Simon Dooms, Babak Loni, Domonkos Tikk. 387-388 [doi]
• Controlled experimentation in recommendations, ranking & response predictionYa Xu, Rajesh Parekh, Juliette Aurisset. 389 [doi]
• First workshop on recommendation systems for television and online video: (RecSysTV 2014)Danny Bickson, John Hannon, Jan Neumann, Hassan Sayyadi. 391-392 [doi]
• REDD 2014 - international workshop on recommender systems evaluation: dimensions and designPanagiotis Adamopoulos, Alejandro Bellogín, Pablo Castells, Paolo Cremonesi, Harald Steck. 393-394 [doi]
• The sixth ACM RecSys workshop on recommender systems and the social webDietmar Jannach, Jill Freyne, Werner Geyer, Ido Guy, Andreas Hotho, Bamshad Mobasher. 395 [doi]
• The recommender problem revisitedXavier Amatriain. 397-398 [doi]
• Personalized location recommendation on location-based social networksHuiji Gao, Jiliang Tang, Huan Liu. 399-400 [doi]
• Tutorial on cross-domain recommender systemsIván Cantador, Paolo Cremonesi. 401-402 [doi]
• Social recommender system tutorialIdo Guy, Werner Geyer. 403-404 [doi]
• Hybridisation techniques for cold-starting context-aware recommender systemsMatthias Braunhofer. 405-408 [doi]
• Moving beyond linearity and independence in top-N recommender systemsEvangelia Christakopoulou. 409-412 [doi]
• Enhancing personalization and learner engagement through context-aware recommendation in TELBetty Mayeku. 413-415 [doi]
• Improving recommender systems: user roles and lifecyclesTien T. Nguyen. 417-420 [doi]
• Modeling the effect of people's preferences and social forces on adopting and sharing itemsAmit Sharma. 421-424 [doi]
• Choicla: towards domain-independent decision support for groups of usersMartin Stettinger. 425-428 [doi]
• Weighted hybrid recommendation for heterogeneous networksFatemeh Vahedian. 429-432 [doi]
• Browser-oriented universal cross-site recommendation and explanation based on user browsing logsYongfeng Zhang. 433-436 [doi]
• Deviation-based and similarity-based contextual SLIM recommendation algorithmsYong Zheng. 437-440 [doi]