A meta-learning perspective on cold-start recommendations for items

Advances in Neural Information Processing Systems

Volumn 2017-December, Issue , 2017, Pages 6905-6915

  Vartak, Manasi ^a   Thiagarajan, Arvind ^b   Miranda, Conrado ^b   Bratman, Jeshua ^b   Larochelle, Hugo ^c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b TWITTER INC   (United States)

^c GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEEP NEURAL NETWORKS; FACTORIZATION; NEURAL NETWORKS;

COLD START PROBLEMS; COLD-START RECOMMENDATIONS; LINEAR CLASSIFIERS; MATRIX FACTORIZATIONS; META LEARNING STRATEGY; PRODUCT RECOMMENDATION; PRODUCTION MODELS; REAL-WORLD PROBLEM;

NETWORK ARCHITECTURE;

EID: 85046997062     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. "never miss important tweets from people you follow". https://blog.twitter.com/2016/never-miss-important-tweets-from-people-you-follow. Accessed: 2017-05-03.
2. Using deep learning at scale in twitter's timelines. https://blog.twitter.com/2017/using-deep-learning-at-scale-in-twitter-s-timelines. Accessed: 2017-05-09.
3. D. Agarwal and B.-C. Chen. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 19-28. ACM, 2009.
4. P. Bachman, A. Sordoni, and A. Trischler. Learning algorithms for active learning. In D. Precup and Y. W. Teh, editors, Proceedings of the 34th International Conference on Machine Learning, Volume 70 of Proceedings of Machine Learning Research, pages 301-310, International Convention Centre, Sydney, Australia, 06-11 Aug 2017. PMLR.
5. L. Charlin, R. S. Zemel, and H. Larochelle. Leveraging user libraries to bootstrap collaborative filtering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 173-182. ACM, 2014.
6. P. Covington, J. Adams, and E. Sargin. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 191-198. ACM, 2016.
7. A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web, pages 271-280. ACM, 2007.
8. X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, et al. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, pages 1-9. ACM, 2014.
9. B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv: 1511.06939, 2015.
10. B. Hidasi, M. Quadrana, A. Karatzoglou, and D. Tikk. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 241-248. ACM, 2016.
11. L. Hong, A. S. Doumith, and B. D. Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 557-566. ACM, 2013.
12. D. Kim, C. Park, J. Oh, S. Lee, and H. Yu. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 233-240. ACM, 2016.
13. G. Koch. Siamese Neural Networks for One-Shot Image Recognition. PhD thesis, University of Toronto, 2015.
14. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426-434. ACM, 2008.
15. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8), 2009.
16. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266): 1332-1338, 2015.
17. X. N. Lam, T. Vu, T. D. Le, and A. D. Duong. Addressing cold-start problem in recommendation systems. In Proceedings of the 2nd international conference on Ubiquitous information management and communication, pages 208-211. ACM, 2008.
18. C. Lemke, M. Budka, and B. Gabrys. Metalearning: a survey of trends and technologies. Artificial intelligence review, 44(1): 117-130, 2015.
19. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1): 76-80, 2003.
20. J. Liu, P. Dolan, and E. R. Pedersen. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces, pages 31-40, 2010.
21. P. Lops, M. De Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In Recommender systems handbook, pages 73-105. Springer, 2011.
22. T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka. Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. Computer Vision-ECCV 2012, pages 488-501, 2012.
23. A. Mnih and R. R. Salakhutdinov. Probabilistic matrix factorization. In Advances in neural information processing systems, pages 1257-1264, 2008.
24. S. Ravi and H. Larochelle. Optimization as a model for few-shot learning. ICLR, 2017.
25. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. Meta-learning with memory-augmented neural networks. In International conference on machine learning, pages 1842-1850, 2016.
26. J. Snell, K. Swersky, and R. S. Zemel. Prototypical networks for few-shot learning. CoRR, abs/1703.05175, 2017.
27. D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In Proceedings of the 18th international conference on World wide web, pages 111-120. ACM, 2009.
28. R. Vilalta and Y. Drissi. A perspective view and survey of meta-learning. Artificial Intelligence Review, 18(2): 77-95, 2002.
29. O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al. Matching networks for one shot learning. In Advances in Neural Information Processing Systems, pages 3630-3638, 2016.
30. C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 448-456. ACM, 2011.
31. M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Póczos, R. Salakhutdinov, and A. J. Smola. Deep sets. CoRR, abs/1703.06114, 2017.