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Practical Diversified Recommendations on YouTube with Determinantal Point Processes

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Practical Diversified Recommendations on YouTube with Determinantal Point Processes CIKM’18, October 2018, Turin, Italy Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). http://tensorflow.org/ Software available from tensorflow.org. [2] Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009. Diversifying Search Results. In Conference on Web Search and Data Mining (WSDM). http://doi.acm.org/10.1145/1498759.1498766 [3] R. Bardenet and M. Titsias. 2015. Inference for Determinantal Point Processes Without Spectral Knowledge. In Neural Information Processing Systems (NIPS). [4] A. Borodin. 2009. Determinantal point processes. ArXiv e-prints (2009). https: //arxiv.org/abs/0911.1153 [5] Jaime Carbonell and Jade Goldstein. 1998. The Use of MMR, Diversity-based Reranking for Reordering Documents and Producing Summaries. In Conference on Research and Development in Information Retreival (SIGIR). http://doi.acm. org/10.1145/290941.291025 [6] Olivier Chapelle, Shihao Ji, Ciya Liao, Emre Velipasaoglu, Larry Lai, and Su- Lin Wu. 2011. Intent-based Diversification of Web Search Results: Metrics and Algorithms. Information Retrieval 14, 6 (2011), 572–592. http://dx.doi.org/10. 1007/s10791- 011- 9167- 7 [7] Laming Chen, Guoxin Zhang, and Hanning Zhou. 2017. Improving the Diver- sity of Top-N Recommendation via Determinantal Point Process. In Large Scale Recommendation Systems Workshop at the Conference on Recommender Systems (RecSys). http://arxiv.org/abs/1709.05135 [8] Charles L.A. Clarke, Maheedhar Kolla, Gordon V. Cormack, Olga Vechtomova, Azin Ashkan, Stefan Büttcher, and Ian MacKinnon. 2008. Novelty and Diversity in Information Retrieval Evaluation. In Conference on Research and Development inInformationRetreival(SIGIR). http://doi.acm.org/10.1145/1390334.1390446 [9] Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Conference on Recommender Systems (RecSys). [10] Van Dang and W. Bruce Croft. 2012. Diversity by Proportionality: An Election- based Approach to Search Result Diversification. In Conference on Research and Development in Information Retreival (SIGIR). http://doi.acm.org/10.1145/2348283. 2348296 [11] Marina Drosou and Evaggelia Pitoura. 2010. Search Result Diversification. SIG- MOD Record 39, 1 (2010), 41–47. http://doi.acm.org/10.1145/1860702.1860709 [12] Mike Gartrell, Ulrich Paquet, and Noam Koenigstein. 2016. Bayesian Low-Rank Determinantal Point Processes. In Conference on Recommender Systems (RecSys). [13] J. Gillenwater. 2014. Approximate Inference for Determinantal Point Processes. Ph.D. Dissertation. University of Pennsylvania. [14] J. Gillenwater, A. Kulesza, E. Fox, and B. Taskar. 2014. Expectation-Maximization for Learning Determinantal Point Processes. In Neural Information Processing Systems (NIPS). [15] Sreenivas Gollapudi and Aneesh Sharma. 2009. An Axiomatic Approach for Result Diversification. In Conference on the World Wide Web (WWW). http: //doi.acm.org/10.1145/1526709.1526761 [16] Yoshinori Hijikata, Takuya Shimizu, and Shogo Nishida. 2009. Discovery-oriented Collaborative Filtering for Improving User Satisfaction. In Conference on Intelli- gent User Interfaces (IUI). http://doi.acm.org/10.1145/1502650.1502663 [17] Henning Hohnhold, Deirdre O’Brien, and Diane Tang. 2015. Focus on the Long- Term: It’s better for Users and Business. In Conference on Knowledge Discovery and Data Mining (KDD). http://dl.acm.org/citation.cfm?doid=2783258.2788583 [18] Chun-Wa Ko, Jon Lee, and Maurice Queyranne. 1995. An Exact Algorithm for Maximum Entropy Sampling. Operations Research 43, 4 (1995), 684–691. http://www.jstor.org/stable/171694 [19] Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (2009), 30–37. http: //dx.doi.org/10.1109/MC.2009.263 [20] Alex Kulesza and Ben Taskar. 2011. k-DPPs: Fixed-Size Determinantal Point Processes. In International Conference on Machine Learning (ICML). [21] Alex Kulesza and Ben Taskar. 2011. Learning Determinantal Point Processes. In Conference on Uncertainty in Artificial Intelligence (UAI). [22] Alex Kulesza and Ben Taskar. 2012. Determinantal Point Processes for Machine Learning. Foundations and Trends in Machine Learning 5, 2-3 (2012), 123–286. http://dx.doi.org/10.1561/2200000044 [23] YoungOk Kwon and Gediminas Adomavicius. 2007. New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intelligent Systems 22 (2007), 48–55. [24] Neal Lathia, Stephen Hailes, Licia Capra, and Xavier Amatriain. 2010. Temporal Diversity in Recommender Systems. In Conference on Research and Development inInformationRetreival(SIGIR). http://doi.acm.org/10.1145/1835449.1835486 [25] Hui Lin and Jeff Bilmes. 2011. A Class of Submodular Functions for Document Summarization. In Annual Meeting of the Association for Computational Linguis- tics: Human Language Technologies (HLT). http://dl.acm.org/citation.cfm?id= 2002472.2002537 [26] Zelda Mariet and Suvrit Sra. 2015. Fixed-Point Algorithms for Learning Determi- natal Point Processes. In International Conference on Machine Learning (ICML). [27] Zelda Mariet and Suvrit Sra. 2016. Kronecker Determinantal Point Processes. In Neural Information Processing Systems (NIPS). [28] Julian McAuley, Rahul Pandey, and Jure Leskovec. 2015. Inferring Networks of Substitutable and Complementary Products. In Conference on Knowledge Discov- ery and Data Mining (KDD). http://doi.acm.org/10.1145/2783258.2783381 [29] Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems. In CHI Extended Abstracts on Human Factors in Computing Systems. http://doi.acm.org/ 10.1145/1125451.1125659 [30] H. Nassif, K.O. Cansizlar, M. Goodman, and S.V.N. Vishwanathan. 2016. Diversi- fying Music Recommendations. In International Conference on Machine Learning (ICML) Workshop. [31] G. Nemhauser, L. Wolsey, and M. Fisher. 1978. An Analysis of Approximations for Maximizing Submodular Set Functions I. Mathematical Programming 14 (1978), 265–294. [32] Yonathan Perez, Michael Schueppert, Matthew Lawlor, and Shaunak Kishore. 2015. Category-Driven Approach for Local Related Business Recommendations. In Conference on Information and Knowledge Management (CIKM). 73–82. http: //dl.acm.org/citation.cfm?doid=2806416.2806495 [33] Davood Rafiei, Krishna Bharat, and Anand Shukla. 2010. Diversifying Web Search Results. In Conference on the World Wide Web (WWW). http://doi.acm.org/10. 1145/1772690.1772770 [34] Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Conference on Computer Supported Cooperative Work (CSCW). http: //doi.acm.org/10.1145/192844.192905 [35] Rodrygo L.T. Santos, Craig Macdonald, and Iadh Ounis. 2010. Exploiting Query Reformulations for Web Search Result Diversification. In Conference on the World Wide Web (WWW). http://doi.acm.org/10.1145/1772690.1772780 [36] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based Collaborative Filtering Recommendation Algorithms. In Conference on the World Wide Web (WWW). http://doi.acm.org/10.1145/371920.372071 [37] Choon Hui Teo, Houssam Nassif, Daniel Hill, Sriram Srinivasan, Mitchell Good- man, Vijai Mohan, and S.V.N. Vishwanathan. 2016. Adaptive, Personalized Di- versity for Visual Discovery. In Conference on Recommender Systems (RecSys). http://doi.acm.org/10.1145/2959100.2959171 [38] Sebastian Tschiatschek, Adish Singla, and Andreas Krause. 2017. Selecting Se- quences of Items via Submodular Maximization. In Conference on Artificial Intel- ligence (AAAI). [39] Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, and Pablo Castells. 2014. Coverage, Redundancy and Size-awareness in Genre Diversity for Recommender Systems. In Conference on Recommender Systems (RecSys). http://doi.acm.org/10. 1145/2645710.2645743 [40] Erik Vee, Utkarsh Srivastava, Jayavel Shanmugasundaram, Prashant Bhat, and Sihem Amer Yahia. 2008. Efficient Computation of Diverse Query Results. In International Conference on Data Engineering (ICDE). http://dx.doi.org/10.1109/ ICDE.2008.4497431 [41] Cong Yu, Laks Lakshmanan, and Sihem Amer-Yahia. 2009. It Takes Variety to Make a World: Diversification in Recommender Systems. In Conference on Extending Database Technology (EDBT). http://doi.acm.org/10.1145/1516360. 1516404 [42] Cheng Xiang Zhai, William W. Cohen, and John Lafferty. 2003. Beyond In- dependent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. In Conference on Research and Development in Information Retreival (SIGIR). http://doi.acm.org/10.1145/860435.860440 [43] Mi Zhang and Neil Hurley. 2008. Avoiding Monotony: Improving the Diversity of Recommendation Lists. In Conference on Recommender Systems (RecSys). http: //doi.acm.org/10.1145/1454008.1454030 [44] Jiaqian Zheng, Xiaoyuan Wu, Junyu Niu, and Alvaro Bolivar. 2009. Substitutes or Complements: Another Step Forward in Recommendations. In Conference on Electronic Commerce (EC). http://doi.acm.org/10.1145/1566374.1566394 [45] Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving Recommendation Lists Through Topic Diversification. In Conference on the World Wide Web (WWW). http://doi.acm.org/10.1145/1060745.1060754

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