| 1 | | Query-based Music Recommendations via Preference Embedding | | 0 | | ChihMing Chen, MingFeng Tsai, YiHsuan Yang, YuChing Lin | |
| 2 | | Personalization for Google Now: User Understanding and Application to Information Recommendation and Exploration | | 0 | | Shashi Thakur | |
| 3 | | Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks | | 0 | | Evgeny Frolov, Ivan V. Oseledets | |
| 4 | | Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion | | 0 | | Daniel A. Galron, Marie Jacob, Natraj Srinivasan, Paul Wang, Ryan Snyder, Stephen Neola, Yuri M. Brovman | |
| 5 | | Bayesian Personalized Ranking with Multi-Channel User Feedback | | 0 | | Alan Hanjalic, Babak Loni, Martha A. Larson, Roberto Pagano | |
| 6 | | Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation | | 0 | | Alexis Conneau, Elena Smirnova, Flavian Vasile | |
| 7 | | Multi-Word Generative Query Recommendation Using Topic Modeling | | 0 | | Chirag Shah, Matthew Mitsui | |
| 8 | | A Scalable Approach for Periodical Personalized Recommendations | | 0 | | Ish Rishabh, John Carnahan, Zhen Qin | |
| 9 | | Ask the GRU: Multi-task Learning for Deep Text Recommendations | | 0 | | Andrew McCallum, David Belanger, Trapit Bansal | |
| 10 | | Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback | | 0 | | Eugenio Di Sciascio, Ignacio FernándezTobías, Iván Cantador, Paolo Tomeo, Tommaso Di Noia | |
| 11 | | Mechanism Design for Personalized Recommender Systems | | 0 | | Aris FilosRatsikas, Chang Liu, Pingzhong Tang, Qingpeng Cai | |
| 12 | | Crowd-Based Personalized Natural Language Explanations for Recommendations | | 0 | | F. Maxwell Harper, Loren Gilbert Terveen, Shuo Chang | |
| 13 | | Recommender Systems with Personality | | 0 | | Amos Azaria, Jason I. Hong | |
| 14 | | TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation | | 0 | | Hancheng Ge, Haokai Lu, James Caverlee | |
| 15 | | MAPS: A Multi Aspect Personalized POI Recommender System | | 0 | | Ramesh Baral, Tao Li | |
| 16 | | Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach | | 0 | | Rose Catherine, William W. Cohen | |
| 17 | | Mendeley: Recommendations for Researchers | | 0 | | Kris Jack, Maya Hristakeva, Saúl Vargas | |
| 18 | | Multi-corpus Personalized Recommendations on Google Play | | 0 | | Cyrus Master, Levent Koc | |
| 19 | | Conversational Recommendation System with Unsupervised Learning | | 0 | | Roger Jin, Yi Zhang, Yueming Sun, Yunfei Chen | |
| 20 | | Local Item-Item Models For Top-N Recommendation | | 0 | | Evangelia Christakopoulou, George Karypis | |
| 21 | | Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens | | 0 | | Joseph A. Konstan, Raghav Pavan Karumur, Tien T. Nguyen | |
| 22 | | Domain-Aware Grade Prediction and Top-n Course Recommendation | | 0 | | Asmaa Elbadrawy, George Karypis | |
| 23 | | Behaviorism is Not Enough: Better Recommendations through Listening to Users | | 0 | | Martijn C. Willemsen, Michael D. Ekstrand | |
| 24 | | Recommending New Items to Ephemeral Groups Using Contextual User Influence | | 0 | | Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca | |
| 25 | | Item-to-item Recommendations at Pinterest | | 0 | | Stephanie Kaye Rogers | |
| 26 | | A Coverage-Based Approach to Recommendation Diversity On Similarity Graph | | 0 | | Nicolas Usunier, Shameem Puthiya Parambath, Yves Grandvalet | |
| 27 | | Contrasting Offline and Online Results when Evaluating Recommendation Algorithms | | 0 | | Fabio Stella, Marco Rossetti, Markus Zanker | |
| 28 | | Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence | | 0 | | David M. Blei, Dawen Liang, Jaan Altosaar, Laurent Charlin | |
| 29 | | Latent Factor Representations for Cold-Start Video Recommendation | | 0 | | Sharath Chandra Guntuku, Sujoy Roy | |
| 30 | | Addressing Cold Start for Next-song Recommendation | | 0 | | JyhShing Roger Jang, SzuYu Chou, YiHsuan Yang, YuChing Lin | |
| 31 | | Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques | | 0 | | Francesco Ricci, Marko Tkalcic, Saikishore Kalloori | |
| 32 | | Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations | | 0 | | Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Massimo Quadrana | |
| 33 | | Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks | | 0 | | Bartlomiej Twardowski | |
| 34 | | Bayesian Low-Rank Determinantal Point Processes | | 0 | | Mike Gartrell, Noam Koenigstein, Ulrich Paquet | |
| 35 | | Recommending Repeat Purchases using Product Segment Statistics | | 0 | | Kratika Gupta, Pabitra Mitra, Suvodip Dey | |
| 36 | | The Exploit-Explore Dilemma in Music Recommendation | | 0 | | Òscar Celma | |
| 37 | | Topical Semantic Recommendations for Auteur Films | | 0 | | Andreas Lommatzsch, Christian Rakow, Till Plumbaum | |
| 38 | | RecExp: A Semantic Recommender System with Explanation Based on Heterogeneous Information Network | | 0 | | Bai Wang, Chuan Shi, Jian Liu, Jiawei Hu, Philip S. Yu, Zhiqiang Zhang | |
| 39 | | T-RecS: A Framework for a Temporal Semantic Analysis of the ACM Recommender Systems Conference | | 0 | | Fedelucio Narducci, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops, Pierpaolo Basile | |
| 40 | | Gray Sheep, Influential Users, User Modeling and Recommender System Adoption by Startups | | 0 | | Abhishek Srivastava | |
| 41 | | Recommender Systems for Self-Actualization | | 0 | | Bart P. Knijnenburg, Daricia Wilkinson, Saadhika Sivakumar | |
| 42 | | Recommendations with a Purpose | | 0 | | Dietmar Jannach, Gediminas Adomavicius | |
| 43 | | Adaptive, Personalized Diversity for Visual Discovery | | 0 | | Choon Hui Teo, Daniel N. Hill, Houssam Nassif, Mitchell Goodman, S. V. N. Vishwanathan, Sriram Srinivasan, Vijai Mohan | |
| 44 | | Field-aware Factorization Machines for CTR Prediction | | 0 | | ChihJen Lin, WeiSheng Chin, Yong Zhuang, YuChin Juan | |
| 45 | | Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization | | 0 | | Alessandro Bozzon, Jie Yang, Jie Zhang, Zhu Sun | |
| 46 | | HCI for Recommender Systems: the Past, the Present and the Future | | 0 | | André Calero Valdez, Katrien Verbert, Martina Ziefle | |
| 47 | | Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive Models | | 0 | | Olfa Nasraoui, Patrick Shafto | |
| 48 | | Gaze Prediction for Recommender Systems | | 0 | | F. Maxwell Harper, Joseph A. Konstan, Qian Zhao, Shuo Chang | |
| 49 | | ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud | | 0 | | Cataldo Musto, Fedelucio Narducci, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops | |
| 50 | | Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing | | 0 | | Dong Wang, Jermaine Marshall | |
| 51 | | Deep Neural Networks for YouTube Recommendations | | 0 | | Emre Sargin, Jay Adams, Paul Covington | |
| 52 | | Past, Present, and Future of Recommender Systems: An Industry Perspective | | 0 | | Justin Basilico, Xavier Amatriain | |
| 53 | | Algorithms Aside: Recommendation As The Lens Of Life | | 0 | | Andreas Lommatzsch, Andrew M. Demetriou, Anna Zacchi, Ayse Göker, Daniel Kohlsdorf, Davide Malagoli, Domonkos Tikk, Franca Garzotto, Francesco Ricci, Frank Hopfgartner, Jasminko Novak, JeanYves Le Moine, Kristaps Dobrajs, Mario Scriminaci, Marko Tkalcic, Martha A. Larson, Omar Alonso, Paolo Cremonesi, Tamas Motajcsek, Thuy Ngoc Nguyen | |
| 54 | | A Package Recommendation Framework for Trip Planning Activities | | 0 | | Dominique Lenne, Idir Benouaret | |
| 55 | | The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems | | 0 | | Alexandros Karatzoglou, Balázs Hidasi, Domonkos Tikk, Martha A. Larson, Massimo Quadrana, Paolo Cremonesi, Roberto Pagano | |
| 56 | | Convolutional Matrix Factorization for Document Context-Aware Recommendation | | 0 | | Chanyoung Park, Dong Hyun Kim, Hwanjo Yu, Jinoh Oh, Sungyoung Lee | |
| 57 | | Getting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender Systems | | 0 | | Alexander Ilic, Denis Vuckovac, Julia Wamsler, Martin Natter | |
| 58 | | Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks | | 0 | | Hassan Abassi, Roy Levin, Uzi Cohen | |
| 59 | | STAR: Semiring Trust Inference for Trust-Aware Social Recommenders | | 0 | | Hui Miao, Jennifer Golbeck, John S. Baras, Peixin Gao | |
| 60 | | Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation | | 0 | | Chen Fang, Julian J. McAuley, Ruining He, Zhaowen Wang | |
| 61 | | Using Navigation to Improve Recommendations in Real-Time | | 0 | | Alexander J. Smola, ChaoYuan Wu, Christopher V. Alvino, Justin Basilico | |
| 62 | | Efficient Bayesian Methods for Graph-based Recommendation | | 0 | | Ramon Lopes, Renato M. Assunção, Rodrygo L. T. Santos | |
| 63 | | When Recommendation Systems Go Bad | | 0 | | Evan Estola | |
| 64 | | News Recommendations at scale at Bloomberg Media: Challenges and Approaches | | 0 | | Dhaval Shah, Parth Shah, Pramod Koneru, Rohit Parimi | |
| 65 | | Recommending for the World | | 0 | | Justin Basilico, Yves Raimond | |
| 66 | | Marsbot: Building a Personal Assistant | | 0 | | Max Sklar | |
| 67 | | Music Personalization at Spotify | | 0 | | Brian Whitman, Edward Newett, Kurt Jacobson, Romain Yon, Vidhya Murali | |
| 68 | | Feature Selection For Human Recommenders | | 0 | | Katherine A. Livins | |
| 69 | | Leveraging a Graph-Powered, Real-Time Recommendation Engine to Create Rapid Business Value | | 0 | | Adam Anthony, Ruoming Jin, Yang Xiang, YuKeng Shih | |
| 70 | | Considering Supplier Relations and Monetization in Designing Recommendation Systems | | 0 | | Jan Krasnodebski, John Dines | |
| 71 | | Recommending the World's Knowledge: Application of Recommender Systems at Quora | | 0 | | Lei Yang, Xavier Amatriain | |
| 72 | | A Recommender System to tackle Enterprise Collaboration | | 0 | | Gabriel de Souza Pereira Moreira, Gilmar Alves de Souza | |
| 73 | | RecSys Challenge 2016: Job Recommendations | | 0 | | András A. Benczúr, Daniel Kohlsdorf, Fabian Abel, Martha A. Larson, Róbert Pálovics | |
| 74 | | Engendering Health with Recommender Systems | | 0 | | Alan Said, Bernd Ludwig, Christoph Trattner, David Elsweiler, Hanna Schäfer | |
| 75 | | Group Recommender Systems | | 0 | | Ludovico Boratto | |
| 76 | | Matrix and Tensor Decomposition in Recommender Systems | | 0 | | Panagiotis Symeonidis | |
| 77 | | People Recommendation Tutorial | | 0 | | Ido Guy, Luiz Augusto Pizzato | |
| 78 | | Context-Based IDE Command Recommender System | | 0 | | Marko Gasparic | |
| 79 | | Tutorial: Lessons Learned from Building Real-life Recommender Systems | | 0 | | Deepak Agarwal, Xavier Amatriain | |
| 80 | | Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources | | 0 | | CatalinMihai Barbu | |
| 81 | | Personalized Support for Healthy Nutrition Decisions | | 0 | | Hanna Schäfer | |
| 82 | | Proactive Recommendation Delivery | | 0 | | Adem Sabic | |
| 83 | | Recommender Systems from an Industrial and Ethical Perspective | | 0 | | Dimitris Paraschakis | |
| 84 | | Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization | | 0 | | Bikash Joshi, Franck Iutzeler, MassihReza Amini | |
| 85 | | Mining Information for the Cold-Item Problem | | 0 | | Fatemeh Pourgholamali | |
| 86 | | Automated Machine Learning in the Wild | | 0 | | Claudia Perlich | |
| 87 | | Intent-Aware Diversification Using a Constrained PLSA | | 0 | | Jacek Wasilewski, Neil Hurley | |
| 88 | | Joint User Modeling across Aligned Heterogeneous Sites | | 0 | | Xuezhi Cao, Yong Yu | |
| 89 | | The Value of Online Customer Reviews | | 0 | | Edward C. Malthouse, Georgios Askalidis | |
| 90 | | A Cross-Industry Machine Learning Framework with Explicit Representations | | 0 | | Denise Ichinco, Jana Eggers, Nathan Wilson, Sahil Zubair | |
| 91 | | Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences | | 0 | | Baruch Brutman, Guy Kobrinsky, Ido Tamir, Ronny Lempel, Roy Bass, Yoram Dayagi | |
| 92 | | Observing Group Decision Making Processes | | 0 | | Amra Delic, Francesco Ricci, Hannes Werthner, Julia Neidhardt, Laurens Rook, Markus Zanker, Thuy Ngoc Nguyen | |
| 93 | | Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling | | 0 | | Xuezhi Cao, Yiming Liu, Yong Yu | |
| 94 | | Discovering What You're Known For: A Contextual Poisson Factorization Approach | | 0 | | Haokai Lu, James Caverlee, Wei Niu | |
| 95 | | Representation Learning for Homophilic Preferences | | 0 | | Hady Wirawan Lauw, Trong T. Nguyen | |
| 96 | | Hypothesis Testing: How to Eliminate Ideas as Soon as Possible | | 0 | | Roman Zykov | |
| 97 | | Generating Pseudotransactions for Improving Sparse Matrix Factorization | | 0 | | Agung Toto Wibowo | |