专栏首页专知【推荐系统教程】当机器学习遇到推荐系统,悉尼科技大学Liang Hu博士最新分享

【推荐系统教程】当机器学习遇到推荐系统,悉尼科技大学Liang Hu博士最新分享

【导读】第32届AAAI大会-AAAI 2018将于2月2号-7号在美国新奥尔良召开,悉尼科技大学Liang Hu博士即将在大会作报告“When Advanced Machine Learning Meets Intelligent Recommender Systems” ,主要教读者如何用最前沿的机器学习算法实现智能推荐系统。主要内容包括但不局限于:推荐系统的发展进程、如何用机器学习方法建模异构数据、在推荐系统中使用前沿的机器学习算法、构建先进的推荐系统等。文章对推荐算法的总结较为全面,并介绍最新的技术方法,如果您对推荐系统感兴趣,我们建议您关注大会的日常以及Liang Hu博士精彩的分享。专知内容组整理出他放出的PPT内容,欢迎大家查看。

作者主页:https://sites.google.com/view/lianghu/home

When Advanced Machine Learning Meets Intelligent Recommender Systems

▌教程的目标



如今,人工智能(AI)的复兴已经引起了世界各地的广泛关注。 特别是机器学习方法几乎涉及到所有领域,例如自然语言处理(NLP),计算机视觉(CV)和游戏博弈等。

值得一提的是,推荐系统(RS),可能是最广泛使用的AI系统之一,它已经融入到我们日常生活的各个部分。 在AI时代,最前沿的机器学习方法,如深度学习,已经成为构建推荐系统的首选。 目前的机器学习方法是建立在数据的基础之上的,因此推荐任务可以看作这样一类问题:从数据中学习和推断。

本教程的目标是让学术界和实践人员能够全面了解如何应用最前沿的机器学习方法,以便在各种异构数据和复杂场景中构建新一代推荐系统。 在本教程中,我们将介绍最前沿的机器学习技术及其应用,以构建智能推荐系统。 在本教程之后,读者可以掌握:

  • 深入了解推荐技术的最新发展历程;
  • 机器学习方法如何建模异构数据中的复杂耦合并进行推荐;
  • 基于前沿的机器学习方法的推荐系统的发展;
  • 通过本教程中学到的思想,模型和技术,定制和构建高级RS,使其能够自定义复杂的数据。

▌摘要



传统RS是基于相关数据,如评论,内容和/或社会关系,是独立同分布(IID)。 直观地说,这与现实生活中的数据特征不一致,不能代表相关数据的异质性和耦合关系。 因此,我们采用前沿机器学习的方法,通过耦合相关的异构数据来提高互补性,全面性和上下文(3C)信息的RS。 本教程将分析前沿推荐问题中的数据、挑战和业务需求,并采用非IID视角介绍机器学习方面的最新进展,建模基于上下文(3C)的下一代推荐系统(RS)。 包括RS发展综述和非IID推荐系统,跨域RS,社交RS,多模式RS,多标准RS,情境感知RS和基于分组的RS的高级机器学习,以及将它们组合构成真实的推荐系统。

Slides链接:

https://drive.google.com/open?id=1ghtpuwk9BaE7EPdeuJ7781BsNxS3Ww8c

作者主页:

https://sites.google.com/view/lianghu/home

▌PPT内容



▌参考文献:



书目和综述:

  • Kantor, P. B. (2015). Recommender systems handbook. F. Ricci, L. Rokach, & B. Shapira (Eds.). Berlin, Germany:: Springer.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. http://www.deeplearningbook.org/
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics): Springer-Verlag New York, Inc.
  • Cao, L. (2016). Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting. Engineering, 2(2), 212-224.

数据表示

  • Jian, S, Hu, L, Cao, L & Lu, K. AAAI 2018. Metric-based Auto-Instructor for Learning Mixed Data Representation
  • Mikolov, T., Corrado, G., Chen, K., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

推荐系统中的互补信息

  • Anderson, C. (2006). The long tail: Why the future of business is selling less of more
  • Pan, W., Xiang, E. W., Liu, N. N., & Yang, Q. (2010, July). Transfer Learning in Collaborative Filtering for Sparsity Reduction. In AAAI (Vol. 10, pp. 230-235).
  • Singh, A. P., & Gordon, G. J. (2008, August). Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 650-658). ACM.
  • Marlin, B.M., Zemel, R.S., Roweis, S., and Slaney, M. Collaborative filtering and the missing at random assumption. In Proceeding 23rd Conference on Uncertainty in Artificial Intelligence, 2007.
  • Hu, Y., Koren, Y., and Volinsky, C. Collaborative Filtering for Implicit Feedback Datasets. In Eighth IEEE International Conference on Data Mining, 263-272, 2008.
  • Elkahky, A.M., Song, Y., and He, X. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web, 278-288, 2015.
  • Kim, T., Cha, M., Kim, H., Lee, J., & Kim, J. (2017). Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192.
  • Hu, L., Cao, L., Cao, J., Gu, Z., Xu, G., & Yang, D. (2016). Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains. ACM Transactions on Information Systems (TOIS), 35(2), 13.
  • Ma, H., Yang, H., Lyu, M. R., & King, I. (2008, October). Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 931-940). ACM.
  • Jamali, M., & Ester, M. (2010, September). A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems (pp. 135-142). ACM.
  • Ma, H., Zhou, D., Liu, C., Lyu, M. R., & King, I. (2011, February). Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 287-296). ACM.
  • Wang, X., He, X., Nie, L., & Chua, T. S. (2017). Item Silk Road: Recommending Items from Information Domains to Social Users. arXiv preprint arXiv:1706.03205.

推荐系统中的综合信息

  • Oramas, S., Nieto, O., Sordo, M., & Serra, X. (2017). A deep multimodal approach for cold-start music recommendation. arXiv preprint arXiv:1706.09739.
  • Lynch, C., Aryafar, K., & Attenberg, J. (2016, August). Images don't lie: Transferring deep visual semantic features to large-scale multimodal learning to rank. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 541-548). ACM.
  • Hu, L., Cao, L., Cao, J., Gu, Z., Xu, G., & Wang, J. (2017). Improving the Quality of Recommendations for Users and Items in the Tail of Distribution. ACM Transactions on Information Systems (TOIS), 35(3), 25.

推荐系统中的上下文信息

  • Karatzoglou, A., Amatriain, X., Baltrunas, L., & Oliver, N. (2010, September). Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems (pp. 79-86). ACM.
  • Rendle, S., Gantner, Z., Freudenthaler, C., & Schmidt-Thieme, L. (2011, July). Fast context-aware recommendations with factorization machines. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 635-644). ACM.
  • Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... & Anil, R. (2016, September). Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (pp. 7-10). ACM.
  • Rendle, S., Freudenthaler, C., and Schmidt-Thieme , L. (2010, August). Factorizing Personalized Markov Chains for Next-Basket Recommendation. WWW2010.
  • Hidasi, B., Karatzoglou,A., Baltrunas, L., and Tikk, D. (2016, May). Session-based Recommendations with Recurrent Neural Networks. ICLR2016.
  • Gravity R, B., Quadrana, M., Karatzoglou, A., and Tikk, D. (2016 August). Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. RecSys’2016.
  • Hu, L., Cao, L., Wang, S., Xu, G., Cao, J., & Gu, Z. (2017, January). Diversifying Personalized Recommendation with User-session Context. In IJCAI International Joint Conference on Artificial Intelligence.
  • Wang, S., Hu, L., & Cao, L. (2017, September). Perceiving the Next Choice with Comprehensive Transaction Embeddings for Online Recommendation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases(pp. 285-302). Springer, Cham.
  • Wang, S., Hu, L., & Cao, L. (2018, February). Attention-based Transactional Context Embeddings for Next-Item Recommendation. AAAI2018
  • Loyola, P., Liu, C., and Hirate, Y. (2017 August). Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture. RecSys’2017.
  • Lu, Q., Yang, D., Chen, T., Zhang, W., & Yu, Y. (2011, October). Informative household recommendation with feature-based matrix factorization. In proceedings of the 2nd Challenge on Context-Aware Movie Recommendation (pp. 15-22). ACM.
  • Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., & Cao, W. (2014, July). Deep Modeling of Group Preferences for Group-Based Recommendation. In AAAI (Vol. 14, pp. 1861-1867).
  • Masthoff, J. (2015). Group recommender systems: aggregation, satisfaction and group attributes. In Recommender Systems Handbook (pp. 743-776). Springer US.

真实的推荐系统

  • l Netflix Tech Blog: https://medium.com/netflix-techblog

本文分享自微信公众号 - 专知(Quan_Zhuanzhi)

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2018-02-02

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