AAAI 2018 Tutorial

When Advanced Machine Learning Meets Intelligent Recommender Systems

Speakers: Liang Hu and Songlei Jian

Authors: Liang Hu, Songlei Jian, Prof. Longbing Cao and Prof. Jian Cao

External Authors: Shoujin Wang

Time: 2:00 – 6:00 PM, Saturday, Feb. 3rd

Goal of the Tutorial

Nowadays, the renaissance of artificial intelligence (AI) has attracted huge attention from every corner of the world. Specially, machine learning approaches have deeply involved in AI research in almost all areas, e.g., natural language processing (NLP), computer vision (CV) and game playing.

In particular, recommender systems (RS), as probably one of the most widely used AI systems, has integrated into every part of our daily life. In this AI age, state-of-the-art machine learning approaches, e.g. deep learning, have become the primary choice to model advanced RSs. Current machine learning methods are built on data, therefore the recommendation tasks can be regarded as typical AI problems to learn and infer from data.

The goal of this tutorial aims to enable both academic and practical audience with a comprehensive understanding and relevant techniques of how to apply state-of-the-art machine learning approaches to build more sensible next-generation RSs in contexts with various heterogeneous data and complex relations. In this tutorial, we will present a systematic review and applications of recent advanced machine learning techniques to build real-life intelligent RSs. After this tutorial, the audience can walk away with:

• The insight into recent development and evolution of recommendation techniques;
• The machine learning methods to model complex couplings over heterogeneous recommendation data in a comprehensive way;
• The various development of advanced RSs built on the state-of-the-art machine learning methods;
• The practical approaches to customize and build advanced RSs over audience's own complex data with the ideas, models and techniques learned from this tutorial.

Abstract

Classic RSs are built on the assumption that the relevant data, e.g. ratings, contents and/or social relations, are independent and identical distributed (IID). Intuitively, this is inconsistent with real-life data characteristics, and cannot represent the heterogeneity and coupling relationships over relevant data. Therefore, we employ modern machine learning approaches to enhance RSs with complementary, comprehensive, and contextual (3C) information by coupling relevant heterogeneous data. This tutorial will analyze data, challenges, and business needs in advanced recommendation problems, and take non-IID perspective to introduce recent advances in machine learning to model the 3C-based next-generation RSs. This includes an overall of RS evolution and non-IIDness in recommendation, advanced machine learning for cross-domain RS, social RS, multimodal RS, multi-criteria RS, context-aware RS, and group-based RS, and their integration in building real-life RS.

Slides:

slides are here: PDF

Reference:

• Books & Surveys
  • 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.
  • Cao, L., & Yu, P. S. (2016). Non-IID recommendation theories and systems. IEEE Intell Syst, 31(2), 81-4.
• Referenes In Tutorial
  • Data Representation
    • 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.
  • Complementary Information in Recommender Systems
    • 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.
  • Comprehensive Information in Recommender Systems
    • 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.
  • Context Information in Recommender Systems
    • 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.
    • Jiang, X., Liu, W., Cao, L., & Long, G. (2015, January). Coupled Collaborative Filtering for Context-aware Recommendation. In AAAI (pp. 4172-4173).
    • Li, F., Xu, G., Cao, L., Fan, X., & Niu, Z. (2013, October). CGMF: coupled group-based matrix factorization for recommender system. In International Conference on Web Information Systems Engineering (pp. 189-198). Springer, Berlin, Heidelberg.
  • Real-world Recommender Systems
    • Netflix Tech Blog: https://medium.com/netflix-techblog