Table of Contents

• Recommender Systems and the New New Economics of InformationGeorge Loewenstein. 1 [doi]
• Improving Higher Education: Learning Analytics & Recommender Systems ResearchGeorge Karypis. 2 [doi]
• Personalization is a Two-Way StreetRonny Lempel. 3 [doi]
• Memory Networks for RecommendationJason Weston. 4 [doi]
• Learning to Rank with Trust and Distrust in Recommender SystemsDimitrios Rafailidis, Fabio Crestani. 5-13 [doi]
• Metalearning for Context-aware Filtering: Selection of Tensor Factorization AlgorithmsTiago Cunha 0001, Carlos Soares, André C. P. L. F. Carvalho. 14-22 [doi]
• A Gradient-based Adaptive Learning Framework for Efficient Personal RecommendationYue Ning, Yue Shi, Liangjie Hong, Huzefa Rangwala, Naren Ramakrishnan. 23-31 [doi]
• entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item RecommendationEnrico Palumbo, Giuseppe Rizzo 0002, Raphaël Troncy. 32-36 [doi]
• On Parallelizing SGD for Pairwise Learning to Rank in Collaborative Filtering Recommender SystemsA. Murat Yagci, Tevfik Aytekin, Fikret S. Gürgen. 37-41 [doi]
• Controlling Popularity Bias in Learning-to-Rank RecommendationHiman Abdollahpouri, Robin Burke, Bamshad Mobasher. 42-46 [doi]
• Educational Question Routing in Online Student CommunitiesJakub Macina, Ivan Srba, Joseph Jay Williams, Mária Bieliková. 47-55 [doi]
• The Magic Barrier Revisited: Accessing Natural Limitations of Recommender AssessmentKevin Jasberg, Sergej Sizov. 56-64 [doi]
• Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender SystemAlain Starke, Martijn C. Willemsen, Chris Snijders. 65-73 [doi]
• Evaluating Decision-Aware Recommender SystemsRus M. Mesas, Alejandro Bellogín. 74-78 [doi]
• Using Explainability for Constrained Matrix FactorizationBehnoush Abdollahi, Olfa Nasraoui. 79-83 [doi]
• User Preferences for Hybrid ExplanationsPigi Kouki, James Schaffer, Jay Pujara, John O'Donovan, Lise Getoor. 84-88 [doi]
• Secure Multi-Party Protocols for Item-Based Collaborative FilteringErez Shmueli, Tamir Tassa. 89-97 [doi]
• Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and RecommendationsXiaoying Zhang, Junzhou Zhao, John C. S. Lui. 98-106 [doi]
• Fairness-Aware Group Recommendation with Pareto-EfficiencyXiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma. 107-115 [doi]
• A Novel Recommender System for Helping Marathoners to Achieve a New Personal-BestBarry Smyth, Pádraig Cunningham. 116-120 [doi]
• Recommending Personalized News in Short User SessionsElena Viorica Epure, Benjamin Kille, Jon Espen Ingvaldsen, Rébecca Deneckère, Camille Salinesi, Sahin Albayrak. 121-129 [doi]
• Personalizing Session-based Recommendations with Hierarchical Recurrent Neural NetworksMassimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi. 130-137 [doi]
• 3D Convolutional Networks for Session-based Recommendation with Content FeaturesTrinh Xuan Tuan, Tu Minh Phuong. 138-146 [doi]
• Modeling User Session and Intent with an Attention-based Encoder-Decoder ArchitecturePablo Loyola, Chen Liu, Yu Hirate. 147-151 [doi]
• Sequential User-based Recurrent Neural Network RecommendationsTim Donkers, Benedikt Loepp, Jürgen Ziegler 0001. 152-160 [doi]
• Translation-based RecommendationRuining He, Wang-Cheng Kang, Julian McAuley. 161-169 [doi]
• MPR: Multi-Objective Pairwise RankingRasaq Otunba, Raimi A. Rufai, Jessica Lin 0001. 170-178 [doi]
• An Elementary View on Factorization MachinesSebastian Prillo. 179-183 [doi]
• Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start RecommendationsDeborah Cohen, Michal Aharon, Yair Koren, Oren Somekh, Raz Nissim. 184-192 [doi]
• Additive Co-Clustering with Social Influence for RecommendationXixi Du, Huafeng Liu, Liping Jing. 193-200 [doi]
• Folding: Why Good Models Sometimes Make Spurious RecommendationsDoris Xin, Nicolas Mayoraz, Hubert Pham, Karthik Lakshmanan, John R. Anderson. 201-209 [doi]
• Chemical Reactant Recommendation Using a Network of Organic ChemistryJohn Savage, Akihiro Kishimoto, Beat Buesser, Ernesto Diaz-Aviles, Carlos Alzate. 210-214 [doi]
• Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative FilteringBibek Paudel, Thilo Haas, Abraham Bernstein. 215-223 [doi]
• Geographical Diversification in POI Recommendation: Toward Improved Coverage on Interested AreasJungkyu Han, Hayato Yamana. 224-228 [doi]
• Understanding How People Use Natural Language to Ask for RecommendationsJie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren G. Terveen, F. Maxwell Harper. 229-237 [doi]
• Defining and Supporting Narrative-driven RecommendationToine Bogers, Marijn Koolen. 238-242 [doi]
• Recommending Product Sizes to CustomersVivek Sembium, Rajeev Rastogi, Atul Saroop, Srujana Merugu. 243-250 [doi]
• Practical Lessons from Developing a Large-Scale Recommender System at ZalandoAntonino Freno. 251-259 [doi]
• Exploiting Socio-Economic Models for Lodging Recommendation in the Sharing EconomyRaul Sanchez-Vazquez, Jordan Silva, Rodrygo L. T. Santos. 260-268 [doi]
• Surveying User Reactions to Recommendations Based on Inferences Made by Face Detection TechnologyJennifer Marlow, Jason Wiese. 269-273 [doi]
• An Insurance Recommendation System Using Bayesian NetworksMaleeha Qazi, Glenn M. Fung, Katie J. Meissner, Eduardo R. Fontes. 274-278 [doi]
• Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input/Output NetworksJoan Serrà, Alexandros Karatzoglou. 279-287 [doi]
• TransNets: Learning to Transform for RecommendationRose Catherine, William W. Cohen. 288-296 [doi]
• Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating PredictionSungyong Seo, Jing Huang, Hao Yang, Yan Liu. 297-305 [doi]
• When Recurrent Neural Networks meet the Neighborhood for Session-Based RecommendationDietmar Jannach, Malte Ludewig. 306-310 [doi]
• Recommendation of High Quality Representative Reviews in e-commerceDebanjan Paul, Sudeshna Sarkar, Muthusamy Chelliah, Chetan Kalyan, Prajit Prashant Sinai Nadkarni. 311-315 [doi]
• A Semantic-Aware Profile Updating Model for Text RecommendationHossein Rahmatizadeh Zagheli, Hamed Zamani, Azadeh Shakery. 316-320 [doi]
• A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' ReviewsCataldo Musto, Marco de Gemmis, Giovanni Semeraro, Pasquale Lops. 321-325 [doi]
• Exploring the Semantic Gap for Movie RecommendationsMehdi Elahi, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Leonardo Cella, Stefano Cereda, Paolo Cremonesi. 326-330 [doi]
• Dynamic Scholarly Collaborator Recommendation via Competitive Multi-Agent Reinforcement LearningYang Zhang, Chenwei Zhang, Xiaozhong Liu. 331-335 [doi]
• Rethinking Collaborative Filtering: A Practical Perspective on State-of-the-art Research Based on Real World InsightsNoam Koenigstein. 336-337 [doi]
• Recommendation Applications and Systems at Electronic ArtsMeng Wu, John Kolen, Navid Aghdaie, Kazi A. Zaman. 338 [doi]
• Search Ranking And Personalization at AirbnbMihajlo Grbovic. 339-340 [doi]
• Bootstrapping a Destination Recommender SystemNeal Lathia. 341 [doi]
• Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico, Yves Raimond. 342 [doi]
• Building Recommender Systems for Fashion: Industry Talk AbstractNick Landia. 343 [doi]
• Boosting Recommender Systems with Deep LearningJoão Gomes. 344 [doi]
• Personalization challenges in e-LearningRoberto Turrin. 345 [doi]
• Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi, Benjamin Le, Ganesh Venkataraman. 346-347 [doi]
• Online Learning to Rank for Recommender SystemsDaan Odijk, Anne Schuth. 348 [doi]
• Bandit Algorithms for e-Commerce Recommender Systems: Extended AbstractBjörn Brodén, Mikael Hammar, Bengt J. Nilsson, Dimitris Paraschakis. 349 [doi]
• You are what apps you use: Transfer Learning for Personalized Content and Ad RecommendationZhixian Yan, Lai Wei, Yunshan Lu, Zhongqiang Wu, Bo Tao. 350 [doi]
• CheckInShop.eu: A Sensor-based Recommender System for micro-location MarketingPanagiotis Symeonidis, Stergios Chairistanidis. 351-352 [doi]
• A Research Tool for User Preferences Elicitation with Facial ExpressionsMarko Tkalcic, Nima Maleki, Matevz Pesek, Mehdi Elahi, Francesco Ricci 0001, Matija Marolt. 353-354 [doi]
• Data-Driven Repricing Strategies in Competitive Markets: An Interactive Simulation PlatformMartin Boissier 0001, Rainer Schlosser, Nikolai Podlesny, Sebastian Serth, Marvin Bornstein, Johanna Latt, Jan Lindemann, Jan Selke, Matthias Uflacker. 355-357 [doi]
• Pokedem: an Automatic Social Media Management ApplicationFrancesco Corcoglioniti, Claudio Giuliano, Yaroslav Nechaev, Roberto Zanoli. 358-359 [doi]
• Citolytics: A Link-based Recommender System for WikipediaMalte Schwarzer, Corinna Breitinger, Moritz Schubotz, Norman Meuschke, Bela Gipp. 360-361 [doi]
• Visual Analysis of Recommendation PerformanceLudovik Çoba, Panagiotis Symeonidis, Markus Zanker. 362-363 [doi]
• PathRec: Visual Analysis of Travel Route RecommendationsDawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy. 364-365 [doi]
• The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning: (RecSysKTL)Yong Zheng, Weike Pan, Shaghayegh (Sherry) Sahebi, Ignacio Fernández. 366-367 [doi]
• The 1st International Workshop on Temporal Reasoning in Recommender SystemsMária Bieliková, Veronika Bogina, Tsvi Kuflik, Roy Sasson. 368-369 [doi]
• DLRS 2017: Second Workshop on Deep Learning for Recommender SystemsBalázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Sander Dieleman, Bracha Shapira, Domonkos Tikk. 370-371 [doi]
• RecSys Challenge 2017: Offline and Online EvaluationFabian Abel, Yashar Deldjoo, Mehdi Elahi, Daniel Kohlsdorf. 372-373 [doi]
• Second Workshop on Health Recommender Systems: (HealthRecSys 2017)David Elsweiler, Santiago Hors-Fraile, Bernd Ludwig, Alan Said, Hanna Schäfer, Christoph Trattner, Helma Torkamaan, André Calero Valdez. 374-375 [doi]
• KidRec: Children & Recommender Systems: Workshop Co-located with ACM Conference on Recommender Systems (RecSys 2017)Jerry Alan Fails, Maria Soledad Pera, Franca Garzotto, Mirko Gelsomini. 376-377 [doi]
• VAMS 2017: Workshop on Value-Aware and Multistakeholder RecommendationRobin Burke, Gediminas Adomavicius, Ido Guy, Jan Krasnodebski, Luiz Augusto Pizzato, Yi Zhang, Himan Abdollahpouri. 378-379 [doi]
• Workshop on Recommendation in Complex Scenarios: (ComplexRec 2017)Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin. 380-381 [doi]
• FATREC Workshop on Responsible RecommendationMichael D. Ekstrand, Amit Sharma. 382-383 [doi]
• RecSys'17 Joint Workshop on Interfaces and Human Decision Making for Recommender SystemsPeter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Nava Tintarev, Martijn C. Willemsen. 384-385 [doi]
• RecTour 2017: Workshop on Recommenders in TourismJulia Neidhardt, Daniel R. Fesenmaier, Tsvi Kuflik, Wolfgang Wörndl. 386-387 [doi]
• CitRec 2017: International Workshop on Recommender Systems for CitizensJie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang, Martha Larson. 388-389 [doi]
• LSRS'17: Workshop on Large-Scale Recommender SystemsTao Ye, Denis Parra, Vito Ostuni, Tao Wang. 390-391 [doi]
• New Paths in Music Recommender Systems ResearchMarkus Schedl, Peter Knees, Fabien Gouyon. 392-393 [doi]
• Privacy for Recommender Systems: Tutorial AbstractBart P. Knijnenburg, Shlomo Berkovsky. 394-395 [doi]
• Deep Learning for Recommender SystemsAlexandros Karatzoglou, Balázs Hidasi. 396-397 [doi]
• Product Recommendations Enhanced with ReviewsMuthusamy Chelliah, Sudeshna Sarkar. 398-399 [doi]
• Tutorial on Open Source Online Learning RecommendersRóbert Pálovics, Domokos Kelen, András A. Benczúr. 400-401 [doi]
• Recommending a Sequence of Points of Interest to a Group of Users in a Mobile ContextDaniel Herzog. 402-406 [doi]
• Deep Cross-Domain Fashion RecommendationShatha Jaradat. 407-410 [doi]
• Online Recommender System for Personalized Nutrition AdviceRodrigo Zenun Franco. 411-415 [doi]
• Improving Similarity Measures Using Ontological DataÖzge Sürer. 416-420 [doi]
• Improving the Trustworthiness of Recommendations in Collaborative Filtering under the Belief Function FrameworkRaoua Abdelkhalek. 421-425 [doi]
• Unsupervised Context-Driven Recommendations Based On User ReviewsFrancisco J. Peña. 426-430 [doi]
• The Exploration-Exploitation Trade-off in Interactive Recommender SystemsAndrea Barraza-Urbina. 431-435 [doi]
• User Preferences Analysis Using Visual StimuliPeter Gaspar. 436-440 [doi]
• Constructive RecommendationPaolo Dragone. 441-445 [doi]