Table of Contents

• Five E's: reflecting on the design of recommendationsElizabeth F. Churchill. 1 [doi]
• Scalable structured prediction for richly structured socio-behavioral dataLise Getoor. 2 [doi]
• Recommending social cohesionChristopher Berry. 3 [doi]
• Why I like it: multi-task learning for recommendation and explanationYichao Lu, Ruihai Dong, Barry Smyth. 4-12 [doi]
• Effects of personal characteristics on music recommender systems with different levels of controllabilityYucheng Jin, Nava Tintarev, Katrien Verbert. 13-21 [doi]
• Providing explanations for recommendations in reciprocal environmentsAkiva Kleinerman, Ariel Rosenfeld, Sarit Kraus. 22-30 [doi]
• Explore, exploit, and explain: personalizing explainable recommendations with banditsJames McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, Rishabh Mehrotra. 31-39 [doi]
• Interpreting user inaction in recommender systemsQian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan. 40-48 [doi]
• Impact of item consumption on assessment of recommendations in user studiesBenedikt Loepp, Tim Donkers, Timm Kleemann, Jürgen Ziegler 0001. 49-53 [doi]
• Multistakeholder recommendation with provider constraintsÖzge Sürer, Robin Burke, Edward C. Malthouse. 54-62 [doi]
• Translation-based factorization machines for sequential recommendationRajiv Pasricha, Julian McAuley. 63-71 [doi]
• Exploring recommendations under user-controlled data filteringHongyi Wen, Longqi Yang, Michael Sobolev, Deborah Estrin. 72-76 [doi]
• Quality-aware neural complementary item recommendationYin Zhang, Haokai Lu, Wei Niu, James Caverlee. 77-85 [doi]
• Item recommendation on monotonic behavior chainsMengting Wan, Julian McAuley. 86-94 [doi]
• Deep reinforcement learning for page-wise recommendationsXiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang. 95-103 [doi]
• Causal embeddings for recommendationStephen Bonner, Flavian Vasile. 104-112 [doi]
• Neural gaussian mixture model for review-based rating predictionDong Deng, Liping Jing, Jian Yu, Shaolong Sun, Haofei Zhou. 113-121 [doi]
• Interactive recommendation via deep neural memory augmented contextual banditsYilin Shen, Yue Deng, Avik Ray, Hongxia Jin. 122-130 [doi]
• Optimally balancing receiver and recommended users' importance in reciprocal recommender systemsAkiva Kleinerman, Ariel Rosenfeld, Francesco Ricci 0001, Sarit Kraus. 131-139 [doi]
• HOP-rec: high-order proximity for implicit recommendationJheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai. 140-144 [doi]
• Generation meets recommendation: proposing novel items for groups of usersThanh Vinh Vo, Harold Soh. 145-153 [doi]
• Calibrated recommendationsHarald Steck. 154-162 [doi]
• No more ready-made deals: constructive recommendation for telco service bundlingPaolo Dragone, Giovanni Pellegrini, Michele Vescovi, Katya Tentori, Andrea Passerini. 163-171 [doi]
• Preference elicitation as an optimization problemAnna Sepliarskaia, Julia Kiseleva, Filip Radlinski, Maarten de Rijke. 172-180 [doi]
• Comfride: a smartphone based system for comfortable public transport recommendationRohit Verma, Surjya Ghosh, Saketh Mahankali, Niloy Ganguly, Bivas Mitra, Sandip Chakraborty. 181-189 [doi]
• Understanding user interactions with podcast recommendations delivered via voiceLongqi Yang, Michael Sobolev, Christina Tsangouri, Deborah Estrin. 190-194 [doi]
• time translation to improve recommendations for real-world retailBobby Prévost, Jonathan Laflamme Janssen, Jaime R. Camacaro, Carolina Bessega. 195-199 [doi]
• The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena gamesZhengxing Chen, Truong-Huy D. Nguyen, Yuyu Xu, Christopher Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr. 200-208 [doi]
• Recommending social-interactive games for adults with autism spectrum disorders (ASD)Yiu-Kai Ng, Maria Soledad Pera. 209-213 [doi]
• Sustainability at scale: towards bridging the intention-behavior gap with sustainable recommendationsSabina Tomkins, Steven Isley, Ben London, Lise Getoor. 214-218 [doi]
• What's going on in my city?: recommender systems and electronic participatory budgetingIván Cantador, María E. Cortés-Cediel, Miriam Fernández, Harith Alani. 219-223 [doi]
• How algorithmic confounding in recommendation systems increases homogeneity and decreases utilityAllison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt. 224-232 [doi]
• Enhancing structural diversity in social networks by recommending weak tiesJavier Sanz-Cruzado, Pablo Castells. 233-241 [doi]
• Exploring author gender in book rating and recommendationMichael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, Daniel Kluver. 242-250 [doi]
• Get me the best: predicting best answerers in community question answering sitesRohan Tondulkar, Manisha Dubey, Maunendra Sankar Desarkar. 251-259 [doi]
• On the robustness and discriminative power of information retrieval metrics for top-N recommendationDaniel Valcarce, Alejandro Bellogín, Javier Parapar, Pablo Castells. 260-268 [doi]
• Streamingrec: a framework for benchmarking stream-based news recommendersMichael Jugovac, Dietmar Jannach, Mozhgan Karimi. 269-273 [doi]
• A field study of related video recommendations: newest, most similar, or most relevant?Yifan Zhong, Tahir Lazaro Sousa Menezes, Vikas Kumar, Qian Zhao, F. Maxwell Harper. 274-278 [doi]
• Unbiased offline recommender evaluation for missing-not-at-random implicit feedbackLongqi Yang, Yin Cui, Yuan Xuan, Chenyang Wang, Serge J. Belongie, Deborah Estrin. 279-287 [doi]
• Judging similarity: a user-centric study of related item recommendationsYuan Yao, F. Maxwell Harper. 288-296 [doi]
• Recurrent knowledge graph embedding for effective recommendationZhu Sun, Jie Yang 0028, Jie Zhang 0002, Alessandro Bozzon, Long-Kai Huang, Chi Xu. 297-305 [doi]
• User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithmsLaura Burbach, Johannes Nakayama, Nils Plettenberg, Martina Ziefle, André Calero Valdez. 306-310 [doi]
• Spectral collaborative filteringLei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, Philip S. Yu. 311-319 [doi]
• Categorical-attributes-based item classification for recommender systemsQian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Alex Beutel, Francois Belletti, Ed H. Chi. 320-328 [doi]
• Eliciting pairwise preferences in recommender systemsSaikishore Kalloori, Francesco Ricci 0001, Rosella Gennari. 329-337 [doi]
• Adaptive collaborative topic modeling for online recommendationMarie Al-Ghossein, Pierre-Alexandre Murena, Talel Abdessalem, Anthony Barré, Antoine Cornuéjols. 338-346 [doi]
• Recommendations for chemists: a case studySteven L. Rohall, Margaret Pancost-Heidebrecht, Bill Shirley, Douglas Bacon, Michael A. Tarselli. 347-351 [doi]
• Word2vec applied to recommendation: hyperparameters matterHugo Caselles-Dupré, Florian Lesaint, Jimena Royo-Letelier. 352-356 [doi]
• CF4CF: recommending collaborative filtering algorithms using collaborative filteringTiago Cunha 0001, Carlos Soares, André C. P. L. F. de Carvalho. 357-361 [doi]
• Using citation-context to reduce topic drifting on pure citation-based recommendationAnita Khadka, Petr Knoth. 362-366 [doi]
• Measuring anti-relevance: a study on when recommendation algorithms produce bad suggestionsPablo Sánchez 0001, Alejandro Bellogín. 367-371 [doi]
• RecGAN: recurrent generative adversarial networks for recommendation systemsHomanga Bharadhwaj, Homin Park, Brian Y. Lim. 372-376 [doi]
• A crowdsourcing triage algorithm for geopolitical event forecastingMohammad Rostami, David Huber, Tsai-Ching Lu. 377-381 [doi]
• Large-scale recommendation for portfolio optimizationRobin M. E. Swezey, Bruno Charron. 382-386 [doi]
• Deep neural network marketplace recommenders in online experimentsSimen Eide, Ning Zhou. 387-391 [doi]
• A hierarchical bayesian model for size recommendation in fashionRomain Guigourès, Yuen King Ho, Evgenii Koriagin, Abdul-Saboor Sheikh, Urs Bergmann, Reza Shirvany. 392-396 [doi]
• Psrec: social recommendation with pseudo ratingsYitong Meng, Guangyong Chen, Jiajin Li, Shengyu Zhang. 397-401 [doi]
• Harnessing a generalised user behaviour model for next-POI recommendationDavid Massimo, Francesco Ricci 0001. 402-406 [doi]
• Learning consumer and producer embeddings for user-generated content recommendationWang-Cheng Kang, Julian McAuley. 407-411 [doi]
• Field-aware probabilistic embedding neural network for CTR predictionWeiwen Liu, Ruiming Tang, Jiajin Li, Jinkai Yu, Huifeng Guo, Xiuqiang He, Shengyu Zhang. 412-416 [doi]
• Attentive neural architecture incorporating song features for music recommendationNoveen Sachdeva, Kartik Gupta, Vikram Pudi. 417-421 [doi]
• Decomposing fit semantics for product size recommendation in metric spacesRishabh Misra, Mengting Wan, Julian McAuley. 422-426 [doi]
• Learning to recommend diverse items over implicit feedback on PANDORSumit Sidana, Charlotte Laclau, Massih-Reza Amini. 427-431 [doi]
• Learning within-session budgets from browsing trajectoriesDiane Hu, Raphael Louca, Liangjie Hong, Julian McAuley. 432-436 [doi]
• Kernelized probabilistic matrix factorization for collaborative filtering: exploiting projected user and item graphBithika Pal, Mamata Jenamani. 437-440 [doi]
• A probabilistic model for intrusive recommendation assessmentImen Akermi, Mohand Boughanem, Rim Faiz. 441-445 [doi]
• Trust-based collaborative filtering: tackling the cold start problem using regular equivalenceTomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex. 446-450 [doi]
• Rank and rate: multi-task learning for recommender systemsGuy Hadash, Oren Sar Shalom, Rita Osadchy. 451-454 [doi]
• Audio-visual encoding of multimedia content for enhancing movie recommendationsYashar Deldjoo, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi. 455-459 [doi]
• Efficient online recommendation via low-rank ensemble samplingXiuyuan Lu, Zheng Wen, Branislav Kveton. 460-464 [doi]
• Semantic-based tag recommendation in scientific bookmarking systemsHebatallah A. Mohamed Hassan, Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli. 465-469 [doi]
• CLoSe: Contextualized Location Sequence RecommenderRamesh Baral, S. S. Iyengar, Tao Li 0001, N. Balakrishnan 0001. 470-474 [doi]
• User preference learning in multi-criteria recommendations using stacked auto encodersDharahas Tallapally, Rama Syamala Sreepada, Bidyut Kr. Patra, Korra Sathya Babu. 475-479 [doi]
• Variational learning to rank (VL2R)Keld T. Lundgaard. 480 [doi]
• Adapting session based recommendation for features through transfer learningEven Oldridge. 481 [doi]
• Hulu video recommendation: from relevance to reasoningXiaoran Xu, Laming Chen, Songpeng Zu, Hanning Zhou. 482 [doi]
• Hybrid search: incorporating contextual signals in recommendations at pinterestJenny Liu. 483 [doi]
• Learning content and usage factors simultaneously to reduce clickbaitsArnab Bhadury, Aanchan Mohan. 484 [doi]
• Measuring operational quality of recommendations: industry talk abstractLina Weichbrodt. 485 [doi]
• Building recommender systems with strict privacy boundariesRenaud Bourassa. 486 [doi]
• Artwork personalization at netflixFernando Amat, Ashok Chandrashekar, Tony Jebara, Justin Basilico. 487-488 [doi]
• Conversational content discovery via comcast X1 voice interfaceShahin Sefati, Parsa Saadatpanah, Hassan Sayyadi, Jan Neumann. 489 [doi]
• Connecting sellers and buyers on the world's largest inventoryIdo Guy. 490-491 [doi]
• Extra: explaining team recommendation in networksQinghai Zhou, Liangyue Li, Nan Cao, Norbou Buchler, Hanghang Tong. 492-493 [doi]
• Case recommender: a flexible and extensible python framework for recommender systemsArthur F. Da Costa, Eduardo P. Fressato, Fernando S. Aguiar Neto, Marcelo G. Manzato, Ricardo J. G. B. Campello. 494-495 [doi]
• Tourrec: a tourist trip recommender system for individuals and groupsDaniel Herzog, Christopher Laß, Wolfgang Wörndl. 496-497 [doi]
• Module advisor: a hybrid recommender system for elective module explorationNina Hagemann, Michael P. O'Mahony, Barry Smyth. 498-499 [doi]
• Automating recommender systems experimentation with librec-autoMasoud Mansoury, Robin Burke, Aldo Ordonez-Gauger, Xavier Sepulveda. 500-501 [doi]
• Query-based simple and scalable recommender systems with apache hivemallTakuya Kitazawa, Makoto Yui. 502-503 [doi]
• Towards an open, collaborative REST API for recommender systemsIván García, Alejandro Bellogín. 504-505 [doi]
• Picture-based navigation for diagnosing post-harvest diseases of appleMaximilian Nocker, Gabriele Sottocornola, Markus Zanker, Sanja Baric, Greice Amaral Carneiro, Fabio Stella. 506-507 [doi]
• Cognitive company discoveryAnuradha Bhamidipaty, Daniel Gruen, Justin Platz, John Vergo. 508-509 [doi]
• 2nd workshop on recommendation in complex scenarios (complexrec 2018)Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Casper Petersen. 510-511 [doi]
• DLRS 2018: third workshop on deep learning for recommender systemsBalázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Bracha Shapira, Domonkos Tikk, Flavian Vasile, Sander Dieleman. 512-513 [doi]
• REVEAL 2018: offline evaluation for recommender systemsThorsten Joachims, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile. 514-515 [doi]
• 2nd FATREC workshop: responsible recommendationToshihiro Kamishima, Pierre-Nicolas Schwab, Michael D. Ekstrand. 516 [doi]
• Third international workshop on health recommender systems (healthrecsys 2018)David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schäfer, Helma Torkamaan, Christoph Trattner. 517-518 [doi]
• Recsys'18 joint workshop on interfaces and human decision making for recommender systemsPeter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen. 519-520 [doi]
• Knowledge-aware and conversational recommender systemsVito Walter Anelli, Pierpaolo Basile, Derek G. Bridge, Tommaso Di Noia, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Markus Zanker. 521-522 [doi]
• The 2nd workshop on intelligent recommender systems by knowledge transfer & learning (recsysKTL)Shaghayegh (Sherry) Sahebi, Yong Zheng, Weike Pan, Ignacio Fernández. 523-524 [doi]
• ACM recsys workshop on recommenders in tourism (rectour 2018)Julia Neidhardt, Wolfgang Wörndl, Tsvi Kuflik, Markus Zanker. 525-526 [doi]
• Recsys challenge 2018: automatic music playlist continuationChing-Wei Chen, Paul Lamere, Markus Schedl, Hamed Zamani. 527-528 [doi]
• ACM recsys'18 late-breaking results (posters)Christoph Trattner, Vanessa Murdock, Shuo Chang. 529-530 [doi]
• Concept to code: learning distributed representation of heterogeneous sources for recommendationOmprakash Sonie, Sudeshna Sarkar, Surender Kumar. 531-532 [doi]
• Modularizing deep neural network-inspired recommendation algorithmsLongqi Yang, Eugene Bagdasaryan, Hongyi Wen. 533-534 [doi]
• Emotions and personality in recommender systems: tutorialMarko Tkalcic. 535-536 [doi]
• Multimedia recommender systemsYashar Deldjoo, Markus Schedl, Balázs Hidasi, Peter Knees. 537-538 [doi]
• Sequence-aware recommendationMassimo Quadrana, Paolo Cremonesi. 539-540 [doi]
• Mixed methods for evaluating user satisfactionJean Garcia-Gathright, Christine Hosey, Brian St. Thomas, Ben Carterette, Fernando Diaz 0001. 541-542 [doi]
• Testing a recommender system for self-actualizationDaricia Wilkinson. 543-547 [doi]
• Comparing recommender systems using synthetic dataManel Slokom. 548-552 [doi]
• Towards the next generation of multi-criteria recommender systemsZhe Li. 553-557 [doi]
• SeRenA: a semantic recommender for allGiorgia Di Tommaso. 558-562 [doi]
• Using textual summaries to describe a set of productsKittipitch Kuptavanich. 563-567 [doi]
• Video recommendation using crowdsourced time-sync commentsQing-ping. 568-572 [doi]
• Beyond the top-N: algorithms that generate recommendations for self-actualizationLijie Guo. 573-577 [doi]
• CHAMELEON: a deep learning meta-architecture for news recommender systemsGabriel de Souza Pereira Moreira. 578-583 [doi]