Fetching the paper…
Reading the bibliography…
Novel data sources bring new opportunities to improve the quality of recommender systems and serve as a catalyst for the creation of new paradigms on personalized recommendations.
Click-through Prediction for Advertising in Twitter Timeline. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 2015 , Longbing Cao, Chengqi Zhang, Thorsten Joachims, Geoffrey I. Webb, Dragos D. Margineantu, and Graham Williams (Eds.). ACM, 1959–1968
Cheng Li, Yue Lu, Qiaozhu Mei, Dong Wang, and Sandeep Pandey. 2015 · 1968
Earlier work this paper cites.
Maximum Likelihood from Incomplete Data via the EM Algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977 · 1977
Earlier work this paper cites.
The Multi-Armed Bandit Problem: Decomposition and Computation
Michael N. Katehakis and Arthur F. Veinott Jr. 1987 · 1987
Earlier work this paper cites.
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Ronald J. Williams. 1992 · 1992
Earlier work this paper cites.
Hybrid Collaborative Filtering and Content-Based Filtering for Improved Recommender System. In Computational Science - ICCS 2004, 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 3036) , Marian Bubak, G. Dick van Albada, Peter M. A. Sloot, and Jack J. Dongarra (Eds.). Springer, 295–302
Kyung-Yong Jung, Dong-Hyun Park, and Jung-Hyun Lee. 2004 · 2004
Earlier work this paper cites.
Hybrid Web Recommender Systems. In The Adaptive Web, Methods and Strategies of Web Personalization (Lecture Notes in Computer Science, Vol. 4321) , Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.). Springer, 377–408
Robin D. Burke. 2007 · 2007
Earlier work this paper cites.
Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th International Conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, May 8-12, 2007 , Carey L. Williamson, Mary Ellen Zurko, Peter F. Patel-Schneider, and Prashant J. Shenoy (Eds.). ACM, 521–530
Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007 · 2007
Earlier work this paper cites.
Spatio-temporal models for estimating click-through rate. In Proceedings of the 18th International Conference on World Wide Web, WWW 2009, Madrid, Spain, April 20-24, 2009 . ACM, 21–30
Deepak Agarwal, Bee-Chung Chen, and Pradheep Elango. 2009 · 2009
Earlier work this paper cites.
A case study of behavior-driven conjoint analysis on Yahoo!: front page today module. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009 , John F. Elder IV, Françoise Fogelman-Soulié, Peter A. Flach, and Mohammed Javeed Zaki (Eds.). ACM, 1097–1104
Wei Chu, Seung-Taek Park, Todd Beaupre, Nitin Motgi, Amit Phadke, Seinjuti Chakraborty, and Joe Zachariah. 2009 · 2009
Earlier work this paper cites.
Click-through prediction for news queries. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, Boston, MA, USA, July 19-23, 2009 , James Allan, Javed A. Aslam, Mark Sanderson, ChengXiang Zhai, and Justin Zobel (Eds.). ACM, 347–354
Arnd Christian König, Michael Gamon, and Qiang Wu. 2009 · 2009
Earlier work this paper cites.
Dataset Shift in Machine Learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009 , Jeff A. Bilmes and Andrew Y. Ng (Eds.). AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel , Johannes Fürnkranz and Thorsten Joachims (Eds.). Omnipress, 13–20
Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, and Ralf Herbrich. 2010 · 2010
Earlier work this paper cites.
Factorization Machines. In ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010 , Geoffrey I. Webb, Bing Liu, Chengqi Zhang, Dimitrios Gunopulos, and Xindong Wu (Eds.). IEEE Computer Society, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, February 9-12, 2011 , Irwin King, Wolfgang Nejdl, and Hang Li (Eds.). ACM, 297–306
Lihong Li, Wei Chu, John Langford, and Xuanhui Wang. 2011 · 2011
Earlier work this paper cites.
A unifying view on dataset shift in classification
Jose G. Moreno-Torres, Troy Raeder, Rocío Alaíz-Rodríguez, Nitesh V. Chawla, and Francisco Herrera. 2012 · 2011
Earlier work this paper cites.
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Ivo Grondman, Lucian Busoniu, Gabriel A. D. Lopes, and Robert Babuska. 2012 · 2012
Earlier work this paper cites.
Sparse linear methods with side information for top-n recommendations. In Sixth ACM Conference on Recommender Systems, RecSys ’12, Dublin, Ireland, September 9-13, 2012 , Padraig Cunningham, Neil J. Hurley, Ido Guy, and Sarabjot Singh Anand (Eds.). ACM, 155–162
Xia Ning and George Karypis. 2012 · 2012
Earlier work this paper cites.
OFF-set: one-pass factorization of feature sets for online recommendation in persistent cold start settings. In Seventh ACM Conference on Recommender Systems, RecSys ’13, Hong Kong, China, October 12-16, 2013 , Qiang Yang, Irwin King, Qing Li, Pearl Pu, and George Karypis (Eds.). ACM, 375–378
Michal Aharon, Natalie Aizenberg, Edward Bortnikov, Ronny Lempel, Roi Adadi, Tomer Benyamini, Liron Levin, Ran Roth, and Ohad Serfaty. 2013 · 2013
Earlier work this paper cites.
Understanding the effectiveness of video ads: a measurement study. In Proceedings of the 2013 Internet Measurement Conference, IMC 2013, Barcelona, Spain, October 23-25, 2013 , Konstantina Papagiannaki, P. Krishna Gummadi, and Craig Partridge (Eds.). ACM, 149–162
S. Shunmuga Krishnan and Ramesh K. Sitaraman. 2013 · 2013
Earlier work this paper cites.
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller. 2013 · 2013
Earlier work this paper cites.
Activity ranking in LinkedIn feed. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, New York, NY, USA - August 24 - 27, 2014 , Sofus A. Macskassy, Claudia Perlich, Jure Leskovec, Wei Wang, and Rayid Ghani (Eds.). ACM, 1603–1612
Deepak Agarwal, Bee-Chung Chen, Rupesh Gupta, Joshua Hartman, Qi He, Anand Iyer, Sumanth Kolar, Yiming Ma, Pannagadatta Shivaswamy, Ajit Singh, and Liang Zhang. 2014 · 2014
Earlier work this paper cites.
Frequency capping in online advertising
Niv Buchbinder, Moran Feldman, Arpita Ghosh, and Joseph Naor. 2014 · 2014
Earlier work this paper cites.
Random walks in recommender systems: exact computation and simulations. In 23rd International World Wide Web Conference, WWW ’14, Seoul, Republic of Korea, April 7-11, 2014, Companion Volume , Chin-Wan Chung, Andrei Z. Broder, Kyuseok Shim, and Torsten Suel (Eds.). ACM, 811–816
Colin Cooper, Sang-Hyuk Lee, Tomasz Radzik, and Yiannis Siantos. 2014 · 2014
Earlier work this paper cites.
Online Clustering of Bandits. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014 (JMLR Workshop and Conference Proceedings, Vol. 32) . JMLR.org, 757–765
Claudio Gentile, Shuai Li, and Giovanni Zappella. 2014 · 2014
Earlier work this paper cites.
Diederik P. Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Modeling impression discounting in large-scale recommender systems. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, New York, NY, USA - August 24 - 27, 2014 , Sofus A. Macskassy, Claudia Perlich, Jure Leskovec, Wei Wang, and Rayid Ghani (Eds.). ACM, 1837–1846
Pei Lee, Laks V. S. Lakshmanan, Mitul Tiwari, and Sam Shah. 2014 · 2014
Earlier work this paper cites.
Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore
Jia-Wei Chang, Ming-Che Lee, and Tzone I. Wang. 2016 · 2015
Earlier work this paper cites.
Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks. In Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, Vienna, Austria, September 16-20, 2015 , Hannes Werthner, Markus Zanker, Jennifer Golbeck, and Giovanni Semeraro (Eds.). ACM, 163–170
Fabian Christoffel, Bibek Paudel, Chris Newell, and Abraham Bernstein. 2015 · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
Earlier work this paper cites.
Pointer Networks. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada , Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett (Eds.). 2692–2700
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
Earlier work this paper cites.
RecSys Challenge 2016: Job Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 , Shilad Sen, Werner Geyer, Jill Freyne, and Pablo Castells (Eds.). ACM, 425–426
Fabian Abel, András A. Benczúr, Daniel Kohlsdorf, Martha A. Larson, and Róbert Pálovics. 2016 · 2016
Earlier work this paper cites.
Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 , Shilad Sen, Werner Geyer, Jill Freyne, and Pablo Castells (Eds.). ACM, 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Collaborative Filtering Bandits. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016 , Raffaele Perego, Fabrizio Sebastiani, Javed A. Aslam, Ian Ruthven, and Justin Zobel (Eds.). ACM, 539–548
Shuai Li, Alexandros Karatzoglou, and Claudio Gentile. 2016 · 2016
Earlier work this paper cites.
Modeling User Exposure in Recommendation. In Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11 - 15, 2016 , Jacqueline Bourdeau, Jim Hendler, Roger Nkambou, Ian Horrocks, and Ben Y. Zhao (Eds.). ACM, 951–961
Dawen Liang, Laurent Charlin, James McInerney, and David M. Blei. 2016 · 2016
Earlier work this paper cites.
Bayesian Personalized Ranking with Multi-Channel User Feedback. In Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 , Shilad Sen, Werner Geyer, Jill Freyne, and Pablo Castells (Eds.). ACM, 361–364
Babak Loni, Roberto Pagano, Martha A. Larson, and Alan Hanjalic. 2016 · 2016
Earlier work this paper cites.
User Fatigue in Online News Recommendation. In Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11 - 15, 2016 , Jacqueline Bourdeau, Jim Hendler, Roger Nkambou, Ian Horrocks, and Ben Y. Zhao (Eds.). ACM, 1363–1372
Hao Ma, Xueqing Liu, and Zhihong Shen. 2016 · 2016
Earlier work this paper cites.
RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting. In Proceedings of the 2016 Recommender Systems Challenge, RecSys Challenge 2016, Boston, Massachusetts, USA, September 15, 2016 , Fabian Abel, András A. Benczúr, Daniel Kohlsdorf, Martha A. Larson, and Róbert Pálovics (Eds.). ACM, 10:1–10:4
Andrzej Pacuk, Piotr Sankowski, Karol Wegrzycki, Adam Witkowski, and Piotr Wygocki. 2016 · 2016
Earlier work this paper cites.
Recommendations as Treatments: Debiasing Learning and Evaluation. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 (JMLR Workshop and Conference Proceedings, Vol. 48) , Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 1670–1679
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016 · 2016
Earlier work this paper cites.
Dynamically Integrating Item Exposure with Rating Prediction in Collaborative Filtering. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016 , Raffaele Perego, Fabrizio Sebastiani, Javed A. Aslam, Ian Ruthven, and Justin Zobel (Eds.). ACM, 813–816
Ting-Yi Shih, Ting-Chang Hou, Jian-De Jiang, Yen-Chieh Lien, Chia-Rui Lin, and Pu-Jen Cheng. 2016 · 2016
Earlier work this paper cites.
Using Navigation to Improve Recommendations in Real-Time. In Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 , Shilad Sen, Werner Geyer, Jill Freyne, and Pablo Castells (Eds.). ACM, 341–348
Chao-Yuan Wu, Christopher V. Alvino, Alexander J. Smola, and Justin Basilico. 2016 · 2016
Earlier work this paper cites.
GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016 , Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi (Eds.). ACM, 363–372
XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, and Deepak Agarwal. 2016 · 2016
Earlier work this paper cites.
Predictive Collaborative Filtering with Side Information. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016 , Subbarao Kambhampati (Ed.). IJCAI/AAAI Press, 2385–2391
Feipeng Zhao, Min Xiao, and Yuhong Guo. 2016 · 2016
Earlier work this paper cites.
RecSys Challenge 2017: Offline and Online Evaluation. In Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, August 27-31, 2017 , Paolo Cremonesi, Francesco Ricci, Shlomo Berkovsky, and Alexander Tuzhilin (Eds.). ACM, 372–373
Fabian Abel, Yashar Deldjoo, Mehdi Elahi, and Daniel Kohlsdorf. 2017 · 2017
Cited alongside, same era.
LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017 . ACM, 1397–1406
Fedor Borisyuk, Liang Zhang, and Krishnaram Kenthapadi. 2017 · 2017
Cited alongside, same era.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 3146–3154
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017 · 2017
Cited alongside, same era.
Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning. In WSDM ’22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022 , K. Selcuk Candan, Huan Liu, Leman Akoglu, Xin Luna Dong, and Jiliang Tang (Eds.). ACM, 316–324
Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, and Yongfeng Zhang. 2022 · 2022
Later among the works it cites.
User Response Prediction in Online Advertising
Zhabiz Gharibshah and Xingquan Zhu. 2022 · 2022
Later among the works it cites.
Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation. In SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022 , Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.). ACM, 1185–1195
Shansan Gong and Kenny Q. Zhu. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A recurrent neural network without chaos. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net
Thomas Laurent and James von Brecht. 2017 · 2017
Cited alongside, same era.
Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation. In SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022 , Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.). ACM, 2013–2017
Pengyang Li, Rong Chen, Quan Liu, Jian Xu, and Bo Zheng. 2022 · 2017
Cited alongside, same era.
Related Pins at Pinterest: The Evolution of a Real-World Recommender System. In Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, April 3-7, 2017 , Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 583–592
David C. Liu, Stephanie Kaye Rogers, Raymond Shiau, Dmitry Kislyuk, Kevin C. Ma, Zhigang Zhong, Jenny Liu, and Yushi Jing. 2017 · 2017
Cited alongside, same era.
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever. 2017 · 2017
Cited alongside, same era.
Characterizing context-aware recommender systems: A systematic literature review
Norha M. Villegas, Cristian Sánchez, Javier Díaz-Cely, and Gabriel Tamura. 2018 · 2017
Cited alongside, same era.
Deep Sets. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 3391–3401
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola. 2017 · 2017
Cited alongside, same era.
Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017, Portland, OR, USA, February 25 - March 1, 2017 , Charlotte P. Lee, Steven E. Poltrock, Louise Barkhuus, Marcos Borges, and Wendy A. Kellogg (Eds.). ACM, 1444–1453
Qian Zhao, Gediminas Adomavicius, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2017 · 2017
Cited alongside, same era.
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, and Kate Crawford. 2018 · 2018
Cited alongside, same era.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 (Proceedings of Machine Learning Research, Vol. 80) , Jennifer G. Dy and Andreas Krause (Eds.). PMLR, 1856–1865
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. 2018 · 2018
Cited alongside, same era.
Real-time Short Video Recommendation on Mobile Devices. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 , Mohammad Al Hasan and Li Xiong (Eds.). ACM, 3103–3112
Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang, and Kun Gai. 2022 · 2022
Later among the works it cites.
Advances in Collaborative Filtering
Yehuda Koren, Steffen Rendle, and Robert M. Bell. 2022 · 2022
Later among the works it cites.
An Online Multi-task Learning Framework for Google Feed Ads Auction Models. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 3477–3485
Ning Ma, Mustafa Ispir, Yuan Li, Yongpeng Yang, Zhe Chen, Derek Zhiyuan Cheng, Lan Nie, and Kishor Barman. 2022 · 2022
Later among the works it cites.
Replication of Recommender Systems with Impressions. In Proceedings of the 12th Italian Information Retrieval Workshop 2022, Milan, Italy, June 29-30, 2022 (CEUR Workshop Proceedings, Vol. 3177) , Gabriella Pasi, Paolo Cremonesi, Salvatore Orlando, Markus Zanker, David Massimo, and Gloria Turati (Eds.). CEUR-WS.org
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, and Paolo Cremonesi. 2022a · 2022
Later among the works it cites.
Towards the Evaluation of Recommender Systems with Impressions. In RecSys ’22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022 , Jennifer Golbeck, F. Maxwell Harper, Vanessa Murdock, Michael D. Ekstrand, Bracha Shapira, Justin Basilico, Keld T. Lundgaard, and Even Oldridge (Eds.). ACM, 610–615
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, and Paolo Cremonesi. 2022b · 2022
Later among the works it cites.
Recommender Systems: Techniques, Applications, and Challenges
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2022 · 2022
Later among the works it cites.
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation. In WWW ’22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022 , Frédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Médini (Eds.). ACM, 422–430
Qijie Shen, Hong Wen, Wanjie Tao, Jing Zhang, Fuyu Lv, Zulong Chen, and Zhao Li. 2022 · 2022
Later among the works it cites.
Multi-Armed Bandits in Recommendation Systems: A survey of the state-of-the-art and future directions
Nícollas Silva, Heitor Werneck, Thiago Silva, Adriano C. M. Pereira, and Leonardo Rocha. 2022 · 2022
Later among the works it cites.
ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation. In SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022 , Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.). ACM, 363–372
Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, and Wei Chu. 2022a · 2022
Later among the works it cites.
Pre-Trained Language Models and Their Applications
Haifeng Wang, Jiwei Li, Hua Wu, Eduard Hovy, and Yu Sun. 2023a · 2022
Later among the works it cites.
Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 1868–1877
Jiayin Wang, Weizhi Ma, Jiayu Li, Hongyu Lu, Min Zhang, Biao Li, Yiqun Liu, Peng Jiang, and Shaoping Ma. 2022b · 2022
Later among the works it cites.
A Peep into the Future: Adversarial Future Encoding in Recommendation. In WSDM ’22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022 , K. Selcuk Candan, Huan Liu, Leman Akoglu, Xin Luna Dong, and Jiliang Tang (Eds.). ACM, 1177–1185
Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, and Leyu Lin. 2022 · 2022
Later among the works it cites.
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 4472–4481
Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang, and Kun Gai. 2022 · 2022
Later among the works it cites.
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 , Mohammad Al Hasan and Li Xiong (Eds.). ACM, 3684–3693
Yujing Zhang, Zhangming Chan, Shuhao Xu, Weijie Bian, Shuguang Han, Hongbo Deng, and Bo Zheng. 2022 · 2022
Later among the works it cites.
Combo-Fashion: Fashion Clothes Matching CTR Prediction with Item History. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 4621–4629
Chenxu Zhu, Peng Du, Weinan Zhang, Yong Yu, and Yang Cao. 2022 · 2022
Later among the works it cites.
Reinforcement Learning based Recommender Systems: A Survey
Mohammad Mehdi Afsar, Trafford Crump, and Behrouz H. Far. 2023 · 2023
Closest in time.
Toward Job Recommendation for All. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China . ijcai.org, 5906–5914
Guillaume Bied, Solal Nathan, Elia Perennes, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Christophe Gaillac, and Michèle Sebag. 2023 · 2023
Closest in time.
Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023a · 2023
Closest in time.
Controllable Multi-Objective Re-ranking with Policy Hypernetworks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 , Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, and Jieping Ye (Eds.). ACM, 3855–3864
Sirui Chen, Yuan Wang, Zijing Wen, Zhiyu Li, Changshuo Zhang, Xiao Zhang, Quan Lin, Cheng Zhu, and Jun Xu. 2023b · 2023
Closest in time.
Generative Slate Recommendation with Reinforcement Learning. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023 , Tat-Seng Chua, Hady W. Lauw, Luo Si, Evimaria Terzi, and Panayiotis Tsaparas (Eds.). ACM, 580–588
Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, and Maarten de Rijke. 2023 · 2023
Closest in time.
BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 , Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, and Jieping Ye (Eds.). ACM, 4146–4155
Xiao Hu, Yuan Cheng, Zhi Zheng, Yue Wang, Xinxin Chi, and Hengshu Zhu. 2023 · 2023
Closest in time.
Sampling and noise filtering methods for recommender systems: A literature review
Kirti Jain and Rajni Jindal. 2023 · 2023
Closest in time.
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 , Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, and Jieping Ye (Eds.). ACM, 4497–4506
Xiao Lin, Xiaokai Chen, Linfeng Song, Jingwei Liu, Biao Li, and Peng Jiang. 2023 · 2023
Closest in time.
Knowledge distillation-enhanced multitask framework for recommendation
Wenjie Lu. 2023 · 2023
Closest in time.
News Popularity Beyond the Click-Through-Rate for Personalized Recommendations. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 , Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete (Eds.). ACM, 1396–1405
Ashutosh Nayak, Mayur Garg, and Rajasekhara Reddy Duvvuru Muni. 2023 · 2023
Closest in time.
Incorporating Impressions to Graph-Based Recommenders. In Proceedings of the Workshop on Learning and Evaluating Recommendations with Impressions 2023, Singapore, September 19, 2023 (CEUR Workshop Proceedings, Vol. 3590) , Maurizio Ferrari Dacrema, Justin Basilico, Pablo Castells, Paolo Cremonesi, and Fernando Benjamín Pérez Maurera (Eds.). CEUR-WS.org
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, and Paolo Cremonesi. 2023 · 2023
Closest in time.
Video description: A comprehensive survey of deep learning approaches
Ghazala Rafiq, Muhammad Rafiq, and Gyu Sang Choi. 2023 · 2023
Closest in time.
Slate-Aware Ranking for Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023 , Tat-Seng Chua, Hady W. Lauw, Luo Si, Evimaria Terzi, and Panayiotis Tsaparas (Eds.). ACM, 499–507
Yi Ren, Xiao Han, Xu Zhao, Shenzheng Zhang, and Yan Zhang. 2023 · 2023
Closest in time.
Everyone’s a Winner! On Hyperparameter Tuning of Recommendation Models. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023 , Jie Zhang, Li Chen, Shlomo Berkovsky, Min Zhang, Tommaso Di Noia, Justin Basilico, Luiz Pizzato, and Yang Song (Eds.). ACM, 652–657
Faisal Shehzad and Dietmar Jannach. 2023 · 2023
Closest in time.
Take a Fresh Look at Recommender Systems from an Evaluation Standpoint. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 , Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete (Eds.). ACM, 2629–2638
Aixin Sun. 2023 · 2023
Closest in time.
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 , Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, and Jieping Ye (Eds.). ACM, 5039–5050
Dong Wang, Kavé Salamatian, Yunqing Xia, Weiwei Deng, and Qi Zhang. 2023c · 2023
Closest in time.
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 , Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete (Eds.). ACM, 2935–2944
Kai Wang, Zhene Zou, Minghao Zhao, Qilin Deng, Yue Shang, Yile Liang, Runze Wu, Xudong Shen, Tangjie Lyu, and Changjie Fan. 2023d · 2023
Closest in time.
A Survey on the Fairness of Recommender Systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023b · 2023
Closest in time.
A Bird’s-eye View of Reranking: From List Level to Page Level. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023 , Tat-Seng Chua, Hady W. Lauw, Luo Si, Evimaria Terzi, and Panayiotis Tsaparas (Eds.). ACM, 1075–1083
Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, and Yong Yu. 2023 · 2023
Closest in time.
On the User Behavior Leakage from Recommender System Exposure
Xin Xin, Jiyuan Yang, Hanbing Wang, Jun Ma, Pengjie Ren, Hengliang Luo, Xinlei Shi, Zhumin Chen, and Zhaochun Ren. 2023 · 2023
Closest in time.
Evaluating Recommender Systems: Survey and Framework
Eva Zangerle and Christine Bauer. 2023 · 2023
Closest in time.
Debiasing Recommendation by Learning Identifiable Latent Confounders. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 , Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, and Jieping Ye (Eds.). ACM, 3353–3363
Qing Zhang, Xiaoying Zhang, Yang Liu, Hongning Wang, Min Gao, Jiheng Zhang, and Ruocheng Guo. 2023 · 2023
Closest in time.
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023 , Ingo Frommholz, Frank Hopfgartner, Mark Lee, Michael Oakes, Mounia Lalmas, Min Zhang, and Rodrygo L. T. Santos (Eds.). ACM, 3564–3573
Guanglin Zhou, Chengkai Huang, Xiaocong Chen, Xiwei Xu, Chen Wang, Liming Zhu, and Lina Yao. 2023 · 2023
Closest in time.
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2024 · 2024
Closest in time.