| Deep Neural Networks for YouTube Recommendations |
Paul Covington, Jay Adams, Emre Sargin |
|
|
|
code |
1273 |
| Convolutional Matrix Factorization for Document Context-Aware Recommendation |
Dong Hyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Yu |
|
|
|
code |
404 |
| Field-aware Factorization Machines for CTR Prediction |
YuChin Juan, Yong Zhuang, WeiSheng Chin, ChihJen Lin |
|
|
|
code |
374 |
| Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations |
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, Domonkos Tikk |
|
|
|
code |
255 |
| Ask the GRU: Multi-task Learning for Deep Text Recommendations |
Trapit Bansal, David Belanger, Andrew McCallum |
|
|
|
code |
154 |
| Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence |
Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei |
|
|
|
code |
140 |
| Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation |
Flavian Vasile, Elena Smirnova, Alexis Conneau |
|
|
|
code |
113 |
| Recommendations with a Purpose |
Dietmar Jannach, Gediminas Adomavicius |
|
|
|
code |
79 |
| Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach |
Rose Catherine, William W. Cohen |
|
|
|
code |
77 |
| Bayesian Personalized Ranking with Multi-Channel User Feedback |
Babak Loni, Roberto Pagano, Martha A. Larson, Alan Hanjalic |
|
|
|
code |
73 |
| Local Item-Item Models For Top-N Recommendation |
Evangelia Christakopoulou, George Karypis |
|
|
|
code |
62 |
| Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation |
Ruining He, Chen Fang, Zhaowen Wang, Julian J. McAuley |
|
|
|
code |
56 |
| Crowd-Based Personalized Natural Language Explanations for Recommendations |
Shuo Chang, F. Maxwell Harper, Loren Gilbert Terveen |
|
|
|
code |
52 |
| Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks |
Bartlomiej Twardowski |
|
|
|
code |
51 |
| Domain-Aware Grade Prediction and Top-n Course Recommendation |
Asmaa Elbadrawy, George Karypis |
|
|
|
code |
50 |
| A Coverage-Based Approach to Recommendation Diversity On Similarity Graph |
Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet |
|
|
|
code |
43 |
| Recommender Systems for Self-Actualization |
Bart P. Knijnenburg, Saadhika Sivakumar, Daricia Wilkinson |
|
|
|
code |
42 |
| ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud |
Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro |
|
|
|
code |
40 |
| Contrasting Offline and Online Results when Evaluating Recommendation Algorithms |
Marco Rossetti, Fabio Stella, Markus Zanker |
|
|
|
code |
39 |
| Behaviorism is Not Enough: Better Recommendations through Listening to Users |
Michael D. Ekstrand, Martijn C. Willemsen |
|
|
|
code |
38 |
| Recommending New Items to Ephemeral Groups Using Contextual User Influence |
Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca |
|
|
|
code |
36 |
| Latent Factor Representations for Cold-Start Video Recommendation |
Sujoy Roy, Sharath Chandra Guntuku |
|
|
|
code |
32 |
| Gaze Prediction for Recommender Systems |
Qian Zhao, Shuo Chang, F. Maxwell Harper, Joseph A. Konstan |
|
|
|
code |
32 |
| MAPS: A Multi Aspect Personalized POI Recommender System |
Ramesh Baral, Tao Li |
|
|
|
code |
28 |
| Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing |
Jermaine Marshall, Dong Wang |
|
|
|
code |
28 |
| Music Personalization at Spotify |
Kurt Jacobson, Vidhya Murali, Edward Newett, Brian Whitman, Romain Yon |
|
|
|
code |
28 |
| Past, Present, and Future of Recommender Systems: An Industry Perspective |
Xavier Amatriain, Justin Basilico |
|
|
|
code |
27 |
| Query-based Music Recommendations via Preference Embedding |
ChihMing Chen, MingFeng Tsai, YuChing Lin, YiHsuan Yang |
|
|
|
code |
26 |
| Addressing Cold Start for Next-song Recommendation |
SzuYu Chou, YiHsuan Yang, JyhShing Roger Jang, YuChing Lin |
|
|
|
code |
26 |
| Adaptive, Personalized Diversity for Visual Discovery |
Choon Hui Teo, Houssam Nassif, Daniel N. Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, S. V. N. Vishwanathan |
|
|
|
code |
26 |
| TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation |
Hancheng Ge, James Caverlee, Haokai Lu |
|
|
|
code |
25 |
| HCI for Recommender Systems: the Past, the Present and the Future |
André Calero Valdez, Martina Ziefle, Katrien Verbert |
|
|
|
code |
24 |
| A Package Recommendation Framework for Trip Planning Activities |
Idir Benouaret, Dominique Lenne |
|
|
|
code |
24 |
| Observing Group Decision Making Processes |
Amra Delic, Julia Neidhardt, Thuy Ngoc Nguyen, Francesco Ricci, Laurens Rook, Hannes Werthner, Markus Zanker |
|
|
|
code |
24 |
| Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling |
Yiming Liu, Xuezhi Cao, Yong Yu |
|
|
|
code |
24 |
| STAR: Semiring Trust Inference for Trust-Aware Social Recommenders |
Peixin Gao, Hui Miao, John S. Baras, Jennifer Golbeck |
|
|
|
code |
22 |
| Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion |
Yuri M. Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel A. Galron, Ryan Snyder, Paul Wang |
|
|
|
code |
20 |
| Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens |
Raghav Pavan Karumur, Tien T. Nguyen, Joseph A. Konstan |
|
|
|
code |
20 |
| RecSys Challenge 2016: Job Recommendations |
Fabian Abel, András A. Benczúr, Daniel Kohlsdorf, Martha A. Larson, Róbert Pálovics |
|
|
|
code |
20 |
| Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques |
Saikishore Kalloori, Francesco Ricci, Marko Tkalcic |
|
|
|
code |
19 |
| Matrix and Tensor Decomposition in Recommender Systems |
Panagiotis Symeonidis |
|
|
|
code |
19 |
| The Value of Online Customer Reviews |
Georgios Askalidis, Edward C. Malthouse |
|
|
|
code |
19 |
| Recommender Systems with Personality |
Amos Azaria, Jason I. Hong |
|
|
|
code |
17 |
| Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback |
Ignacio FernándezTobías, Paolo Tomeo, Iván Cantador, Tommaso Di Noia, Eugenio Di Sciascio |
|
|
|
code |
16 |
| The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems |
Roberto Pagano, Paolo Cremonesi, Martha A. Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana |
|
|
|
code |
15 |
| Bayesian Low-Rank Determinantal Point Processes |
Mike Gartrell, Ulrich Paquet, Noam Koenigstein |
|
|
|
code |
14 |
| Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization |
Jie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang |
|
|
|
code |
14 |
| Intent-Aware Diversification Using a Constrained PLSA |
Jacek Wasilewski, Neil Hurley |
|
|
|
code |
14 |
| Using Navigation to Improve Recommendations in Real-Time |
ChaoYuan Wu, Christopher V. Alvino, Alexander J. Smola, Justin Basilico |
|
|
|
code |
13 |
| Recommending the World's Knowledge: Application of Recommender Systems at Quora |
Lei Yang, Xavier Amatriain |
|
|
|
code |
13 |
| Conversational Recommendation System with Unsupervised Learning |
Yueming Sun, Yi Zhang, Yunfei Chen, Roger Jin |
|
|
|
code |
12 |
| Recommender Systems from an Industrial and Ethical Perspective |
Dimitris Paraschakis |
|
|
|
code |
12 |
| Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks |
Evgeny Frolov, Ivan V. Oseledets |
|
|
|
code |
10 |
| Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive Models |
Patrick Shafto, Olfa Nasraoui |
|
|
|
code |
10 |
| Efficient Bayesian Methods for Graph-based Recommendation |
Ramon Lopes, Renato M. Assunção, Rodrygo L. T. Santos |
|
|
|
code |
9 |
| Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks |
Roy Levin, Hassan Abassi, Uzi Cohen |
|
|
|
code |
8 |
| Engendering Health with Recommender Systems |
David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schäfer, Christoph Trattner |
|
|
|
code |
8 |
| Joint User Modeling across Aligned Heterogeneous Sites |
Xuezhi Cao, Yong Yu |
|
|
|
code |
8 |
| Group Recommender Systems |
Ludovico Boratto |
|
|
|
code |
7 |
| Mendeley: Recommendations for Researchers |
Saúl Vargas, Maya Hristakeva, Kris Jack |
|
|
|
code |
6 |
| Tutorial: Lessons Learned from Building Real-life Recommender Systems |
Xavier Amatriain, Deepak Agarwal |
|
|
|
code |
6 |
| Representation Learning for Homophilic Preferences |
Trong T. Nguyen, Hady Wirawan Lauw |
|
|
|
code |
6 |
| Personalization for Google Now: User Understanding and Application to Information Recommendation and Exploration |
Shashi Thakur |
|
|
|
code |
5 |
| Multi-Word Generative Query Recommendation Using Topic Modeling |
Matthew Mitsui, Chirag Shah |
|
|
|
code |
5 |
| Gray Sheep, Influential Users, User Modeling and Recommender System Adoption by Startups |
Abhishek Srivastava |
|
|
|
code |
5 |
| Considering Supplier Relations and Monetization in Designing Recommendation Systems |
Jan Krasnodebski, John Dines |
|
|
|
code |
5 |
| Personalized Support for Healthy Nutrition Decisions |
Hanna Schäfer |
|
|
|
code |
5 |
| Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization |
Bikash Joshi, Franck Iutzeler, MassihReza Amini |
|
|
|
code |
5 |
| Mechanism Design for Personalized Recommender Systems |
Qingpeng Cai, Aris FilosRatsikas, Chang Liu, Pingzhong Tang |
|
|
|
code |
4 |
| Algorithms Aside: Recommendation As The Lens Of Life |
Tamas Motajcsek, JeanYves Le Moine, Martha A. Larson, Daniel Kohlsdorf, Andreas Lommatzsch, Domonkos Tikk, Omar Alonso, Paolo Cremonesi, Andrew M. Demetriou, Kristaps Dobrajs, Franca Garzotto, Ayse Göker, Frank Hopfgartner, Davide Malagoli, Thuy Ngoc Nguyen, Jasminko Novak, Francesco Ricci, Mario Scriminaci, Marko Tkalcic, Anna Zacchi |
|
|
|
code |
4 |
| People Recommendation Tutorial |
Ido Guy, Luiz Augusto Pizzato |
|
|
|
code |
4 |
| A Scalable Approach for Periodical Personalized Recommendations |
Zhen Qin, Ish Rishabh, John Carnahan |
|
|
|
code |
3 |
| Item-to-item Recommendations at Pinterest |
Stephanie Kaye Rogers |
|
|
|
code |
3 |
| Recommending Repeat Purchases using Product Segment Statistics |
Suvodip Dey, Pabitra Mitra, Kratika Gupta |
|
|
|
code |
3 |
| News Recommendations at scale at Bloomberg Media: Challenges and Approaches |
Dhaval Shah, Pramod Koneru, Parth Shah, Rohit Parimi |
|
|
|
code |
3 |
| Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources |
CatalinMihai Barbu |
|
|
|
code |
3 |
| Generating Pseudotransactions for Improving Sparse Matrix Factorization |
Agung Toto Wibowo |
|
|
|
code |
3 |
| The Exploit-Explore Dilemma in Music Recommendation |
Òscar Celma |
|
|
|
code |
2 |
| RecExp: A Semantic Recommender System with Explanation Based on Heterogeneous Information Network |
Jiawei Hu, Zhiqiang Zhang, Jian Liu, Chuan Shi, Philip S. Yu, Bai Wang |
|
|
|
code |
2 |
| Getting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender Systems |
Denis Vuckovac, Julia Wamsler, Alexander Ilic, Martin Natter |
|
|
|
code |
2 |
| Leveraging a Graph-Powered, Real-Time Recommendation Engine to Create Rapid Business Value |
Adam Anthony, YuKeng Shih, Ruoming Jin, Yang Xiang |
|
|
|
code |
2 |
| Mining Information for the Cold-Item Problem |
Fatemeh Pourgholamali |
|
|
|
code |
2 |
| When Recommendation Systems Go Bad |
Evan Estola |
|
|
|
code |
1 |
| Recommending for the World |
Justin Basilico, Yves Raimond |
|
|
|
code |
1 |
| Marsbot: Building a Personal Assistant |
Max Sklar |
|
|
|
code |
1 |
| Context-Based IDE Command Recommender System |
Marko Gasparic |
|
|
|
code |
1 |
| Proactive Recommendation Delivery |
Adem Sabic |
|
|
|
code |
1 |
| Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences |
Ido Tamir, Roy Bass, Guy Kobrinsky, Baruch Brutman, Ronny Lempel, Yoram Dayagi |
|
|
|
code |
1 |
| Discovering What You're Known For: A Contextual Poisson Factorization Approach |
Haokai Lu, James Caverlee, Wei Niu |
|
|
|
code |
1 |
| Multi-corpus Personalized Recommendations on Google Play |
Levent Koc, Cyrus Master |
|
|
|
code |
0 |
| Topical Semantic Recommendations for Auteur Films |
Christian Rakow, Andreas Lommatzsch, Till Plumbaum |
|
|
|
code |
0 |
| T-RecS: A Framework for a Temporal Semantic Analysis of the ACM Recommender Systems Conference |
Fedelucio Narducci, Pierpaolo Basile, Pasquale Lops, Marco de Gemmis, Giovanni Semeraro |
|
|
|
code |
0 |
| Feature Selection For Human Recommenders |
Katherine A. Livins |
|
|
|
code |
0 |
| A Recommender System to tackle Enterprise Collaboration |
Gabriel de Souza Pereira Moreira, Gilmar Alves de Souza |
|
|
|
code |
0 |
| Automated Machine Learning in the Wild |
Claudia Perlich |
|
|
|
code |
0 |
| A Cross-Industry Machine Learning Framework with Explicit Representations |
Denise Ichinco, Sahil Zubair, Jana Eggers, Nathan Wilson |
|
|
|
code |
0 |
| Hypothesis Testing: How to Eliminate Ideas as Soon as Possible |
Roman Zykov |
|
|
|
code |
0 |