Abstract is missing.
- Automated Machine Learning in the WildClaudia Perlich. 1 [doi]
- Personalization for Google Now: User Understanding and Application to Information Recommendation and ExplorationShashi Thakur. 3 [doi]
- Recommendations with a PurposeDietmar Jannach, Gediminas Adomavicius. 7-10 [doi]
- Recommender Systems for Self-ActualizationBart P. Knijnenburg, Saadhika Sivakumar, Daricia Wilkinson. 11-14 [doi]
- A Coverage-Based Approach to Recommendation Diversity On Similarity GraphShameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet. 15-22 [doi]
- A Scalable Approach for Periodical Personalized RecommendationsZhen Qin, Ish Rishabh, John Carnahan. 23-26 [doi]
- Multi-Word Generative Query Recommendation Using Topic ModelingMatthew Mitsui, Chirag Shah. 27-30 [doi]
- Contrasting Offline and Online Results when Evaluating Recommendation AlgorithmsMarco Rossetti, Fabio Stella, Markus Zanker. 31-34 [doi]
- Adaptive, Personalized Diversity for Visual DiscoveryChoon Hui Teo, Houssam Nassif, Daniel Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, S. V. N. Vishwanathan. 35-38 [doi]
- Intent-Aware Diversification Using a Constrained PLSAJacek Wasilewski, Neil Hurley. 39-42 [doi]
- Field-aware Factorization Machines for CTR PredictionYu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin. 43-50 [doi]
- Learning Hierarchical Feature Influence for Recommendation by Recursive RegularizationJie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang. 51-58 [doi]
- Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrenceDawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei. 59-66 [doi]
- Local Item-Item Models For Top-N RecommendationEvangelia Christakopoulou, George Karypis. 67-74 [doi]
- Asynchronous Distributed Matrix Factorization with Similar User and Item Based RegularizationBikash Joshi, Franck Iutzeler, Massih-Reza Amini. 75-78 [doi]
- Query-based Music Recommendations via Preference EmbeddingChih-Ming Chen, Ming-Feng Tsai, Yu-Ching Lin, Yi-Hsuan Yang. 79-82 [doi]
- Joint User Modeling across Aligned Heterogeneous SitesXuezhi Cao, Yong Yu. 83-90 [doi]
- Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations TasksEvgeny Frolov, Ivan Oseledets. 91-98 [doi]
- Latent Factor Representations for Cold-Start Video RecommendationSujoy Roy, Sharath Chandra Guntuku. 99-106 [doi]
- Ask the GRU: Multi-task Learning for Deep Text RecommendationsTrapit Bansal, David Belanger, Andrew McCallum. 107-114 [doi]
- Addressing Cold Start for Next-song RecommendationSzu-Yu Chou, Yi-Hsuan Yang, Jyh-Shing Roger Jang, Yu-Ching Lin. 115-118 [doi]
- Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only FeedbackIgnacio Fernández-Tobías, Paolo Tomeo, Iván Cantador, Tommaso Di Noia, Eugenio Di Sciascio. 119-122 [doi]
- HCI for Recommender Systems: the Past, the Present and the FutureAndré Calero Valdez, Martina Ziefle, Katrien Verbert. 123-126 [doi]
- Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive ModelsPatrick Shafto, Olfa Nasraoui. 127-130 [doi]
- Gaze Prediction for Recommender SystemsQian Zhao, Shuo Chang, F. Maxwell Harper, Joseph A. Konstan. 131-138 [doi]
- Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLensRaghav Pavan Karumur, Tien T. Nguyen, Joseph A. Konstan. 139-142 [doi]
- Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation TechniquesSaikishore Kalloori, Francesco Ricci, Marko Tkalcic. 143-146 [doi]
- Observing Group Decision Making ProcessesAmra Delic, Julia Neidhardt, Thuy Ngoc Nguyen, Francesco Ricci, Laurens Rook, Hannes Werthner, Markus Zanker. 147-150 [doi]
- ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data CloudCataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro. 151-154 [doi]
- The Value of Online Customer ReviewsGeorgios Askalidis, Edward C. Malthouse. 155-158 [doi]
- Mechanism Design for Personalized Recommender SystemsQingpeng Cai, Aris Filos-Ratsikas, Chang Liu, Pingzhong Tang. 159-166 [doi]
- Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social SensingJermaine Marshall, Dong Wang. 167-174 [doi]
- Crowd-Based Personalized Natural Language Explanations for RecommendationsShuo Chang, F. Maxwell Harper, Loren Gilbert Terveen. 175-182 [doi]
- Domain-Aware Grade Prediction and Top-n Course RecommendationAsmaa Elbadrawy, George Karypis. 183-190 [doi]
- Deep Neural Networks for YouTube RecommendationsPaul Covington, Jay Adams, Emre Sargin. 191-198 [doi]
- Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize ConversionYuri M. Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel Galron, Ryan Snyder, Paul Wang. 199-202 [doi]
- A Package Recommendation Framework for Trip Planning ActivitiesIdir Benouaret, Dominique Lenne. 203-206 [doi]
- Recommender Systems with PersonalityAmos Azaria, Jason Hong. 207-210 [doi]
- Past, Present, and Future of Recommender Systems: An Industry PerspectiveXavier Amatriain, Justin Basilico. 211-214 [doi]
- Algorithms Aside: Recommendation As The Lens Of LifeTamas Motajcsek, Jean-Yves Le Moine, Martha Larson, Daniel Kohlsdorf, Andreas Lommatzsch, Domonkos Tikk, Omar Alonso, Paolo Cremonesi, Andrew Demetriou, Kristaps Dobrajs, Franca Garzotto, Ayse Göker, Frank Hopfgartner, Davide Malagoli, Thuy Ngoc Nguyen, Jasminko Novak, Francesco Ricci, Mario Scriminaci, Marko Tkalcic, Anna Zacchi. 215-219 [doi]
- Behaviorism is Not Enough: Better Recommendations through Listening to UsersMichael D. Ekstrand, Martijn C. Willemsen. 221-224 [doi]
- Meta-Prod2Vec: Product Embeddings Using Side-Information for RecommendationFlavian Vasile, Elena Smirnova, Alexis Conneau. 225-232 [doi]
- Convolutional Matrix Factorization for Document Context-Aware RecommendationDong-hyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Yu. 233-240 [doi]
- Parallel Recurrent Neural Network Architectures for Feature-rich Session-based RecommendationsBalázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, Domonkos Tikk. 241-248 [doi]
- The Contextual Turn: from Context-Aware to Context-Driven Recommender SystemsRoberto Pagano, Paolo Cremonesi, Martha Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana. 249-252 [doi]
- Discovering What You're Known For: A Contextual Poisson Factorization ApproachHaokai Lu, James Caverlee, Wei Niu. 253-260 [doi]
- TAPER: A Contextual Tensor-Based Approach for Personalized Expert RecommendationHancheng Ge, James Caverlee, Haokai Lu. 261-268 [doi]
- Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity ModelingYiming Liu, Xuezhi Cao, Yong Yu. 269-272 [doi]
- Modelling Contextual Information in Session-Aware Recommender Systems with Neural NetworksBartlomiej Twardowski. 273-276 [doi]
- Getting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender SystemsDenis Vuckovac, Julia Wamsler, Alexander Ilic, Martin Natter. 277-280 [doi]
- MAPS: A Multi Aspect Personalized POI Recommender SystemRamesh Baral, Tao Li. 281-284 [doi]
- Recommending New Items to Ephemeral Groups Using Contextual User InfluenceElisa Quintarelli, Emanuele Rabosio, Letizia Tanca. 285-292 [doi]
- Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social NetworksRoy Levin, Hassan Abassi, Uzi Cohen. 293-300 [doi]
- STAR: Semiring Trust Inference for Trust-Aware Social RecommendersPeixin Gao, Hui Miao, John S. Baras, Jennifer Golbeck. 301-308 [doi]
- Vista: A Visually, Socially, and Temporally-aware Model for Artistic RecommendationRuining He, Chen Fang, Zhaowen Wang, Julian McAuley. 309-316 [doi]
- Representation Learning for Homophilic PreferencesTrong T. Nguyen, Hady Wirawan Lauw. 317-324 [doi]
- Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming ApproachRose Catherine, William W. Cohen. 325-332 [doi]
- Efficient Bayesian Methods for Graph-based RecommendationRamon Lopes, Renato Assunção, Rodrygo L. T. Santos. 333-340 [doi]
- Using Navigation to Improve Recommendations in Real-TimeChao-Yuan Wu, Christopher V. Alvino, Alexander J. Smola, Justin Basilico. 341-348 [doi]
- Bayesian Low-Rank Determinantal Point ProcessesMike Gartrell, Ulrich Paquet, Noam Koenigstein. 349-356 [doi]
- Recommending Repeat Purchases using Product Segment StatisticsSuvodip Dey, Pabitra Mitra, Kratika Gupta. 357-360 [doi]
- Bayesian Personalized Ranking with Multi-Channel User FeedbackBabak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic. 361-364 [doi]
- Mendeley: Recommendations for ResearchersSaúl Vargas, Maya Hristakeva, Kris Jack. 365 [doi]
- When Recommendation Systems Go BadEvan Estola. 367 [doi]
- News Recommendations at scale at Bloomberg Media: Challenges and ApproachesDhaval Shah, Pramod Koneru, Parth Shah, Rohit Parimi. 369 [doi]
- Marsbot: Building a Personal AssistantMax Sklar. 371 [doi]
- Music Personalization at SpotifyKurt Jacobson, Vidhya Murali, Edward Newett, Brian Whitman, Romain Yon. 373 [doi]
- Recommending for the WorldJustin Basilico, Yves Raimond. 375 [doi]
- The Exploit-Explore Dilemma in Music RecommendationÒscar Celma. 377 [doi]
- Feature Selection For Human RecommendersKatherine A. Livins. 379 [doi]
- Considering Supplier Relations and Monetization in Designing Recommendation SystemsJan Krasnodebski, John Dines. 381-382 [doi]
- A Cross-Industry Machine Learning Framework with Explicit RepresentationsDenise Ichinco, Sahil Zubair, Jana Eggers, Nathan Wilson. 383 [doi]
- Leveraging a Graph-Powered, Real-Time Recommendation Engine to Create Rapid Business ValueAdam Anthony, Yu-Keng Shih, Ruoming Jin, Yang Xiang. 385-386 [doi]
- Hypothesis Testing: How to Eliminate Ideas as Soon as PossibleRoman Zykov. 387 [doi]
- Recommending the World's Knowledge: Application of Recommender Systems at QuoraLei Yang, Xavier Amatriain. 389 [doi]
- Multi-corpus Personalized Recommendations on Google PlayLevent Koc, Cyrus Master. 391 [doi]
- Item-to-item Recommendations at PinterestStephanie Kaye Rogers. 393 [doi]
- A Recommender System to tackle Enterprise CollaborationGabriel de Souza Pereira Moreira, Gilmar Alves de Souza. 395-396 [doi]
- Conversational Recommendation System with Unsupervised LearningYueming Sun, Yi Zhang, Yunfei Chen, Roger Jin. 397-398 [doi]
- Powering Content Discovery through Scalable, Realtime Profiling of Users' Content PreferencesIdo Tamir, Roy Bass, Guy Kobrinsky, Baruch Brutman, Ronny Lempel, Yoram Dayagi. 399-400 [doi]
- RecExp: A Semantic Recommender System with Explanation Based on Heterogeneous Information NetworkJiawei Hu, Zhiqiang Zhang, Jian Liu, Chuan Shi, Philip S. Yu, Bai Wang. 401-402 [doi]
- Topical Semantic Recommendations for Auteur FilmsChristian Rakow, Andreas Lommatzsch, Till Plumbaum. 403-404 [doi]
- T-RecS: A Framework for a Temporal Semantic Analysis of the ACM Recommender Systems ConferenceFedelucio Narducci, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro. 405-406 [doi]
- 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE)Marko Tkalcic, Berardina De Carolis, Marco de Gemmis, Andrej Kosir. 407 [doi]
- Engendering Health with Recommender SystemsDavid Elsweiler, Bernd Ludwig, Alan Said, Hanna Schaefer, Christoph Trattner. 409-410 [doi]
- RecProfile '16: Workshop on Profiling User Preferences for Dynamic, Online, and Real-Time recommendationsRani Nelken. 411-412 [doi]
- RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender SystemsPeter Brusilovsky, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Nava Tintarev, Martijn C. Willemsen. 413-414 [doi]
- RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Oren Sar Shalom, Haggai Roitman, Bracha Shapira. 415-416 [doi]
- RecTour 2016: Workshop on Recommenders in TourismDaniel R. Fesenmaier, Tsvi Kuflik, Julia Neidhardt. 417-418 [doi]
- Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016)Toine Bogers, Marijn Koolen, Cataldo Musto, Pasquale Lops, Giovanni Semeraro. 419-420 [doi]
- LSRS'16: Workshop on Large-Scale Recommender SystemsTao Ye, Danny Bickson, Denis Parra. 421-422 [doi]
- 3rd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2016)Jan Neumann, John Hannon, Claudio Riefolo, Hassan Sayyadi. 423-424 [doi]
- RecSys Challenge 2016: Job RecommendationsFabian Abel, András A. Benczúr, Daniel Kohlsdorf, Martha Larson, Róbert Pálovics. 425-426 [doi]
- Group Recommender SystemsLudovico Boratto. 427-428 [doi]
- Matrix and Tensor Decomposition in Recommender SystemsPanagiotis Symeonidis. 429-430 [doi]
- People Recommendation TutorialIdo Guy, Luiz Augusto Pizzato. 431-432 [doi]
- Tutorial: Lessons Learned from Building Real-life Recommender SystemsXavier Amatriain, Deepak Agarwal. 433 [doi]
- Context-Based IDE Command Recommender SystemMarko Gasparic. 435-438 [doi]
- Generating Pseudotransactions for Improving Sparse Matrix FactorizationAgung Toto Wibowo. 439-442 [doi]
- Gray Sheep, Influential Users, User Modeling and Recommender System Adoption by StartupsAbhishek Srivastava. 443-446 [doi]
- Increasing the Trustworthiness of Recommendations by Exploiting Social Media SourcesCatalin Mihai Barbu. 447-450 [doi]
- Mining Information for the Cold-Item ProblemFatemeh Pourgholamali. 451-454 [doi]
- Personalized Support for Healthy Nutrition DecisionsHanna Schäfer. 455-458 [doi]
- Proactive Recommendation DeliveryAdem Sabic. 459-462 [doi]
- Recommender Systems from an Industrial and Ethical PerspectiveDimitris Paraschakis. 463-466 [doi]