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Deep Learning for Search and Recommender Systems in Practice

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Published:20 August 2020Publication History

ABSTRACT

In this talk, we will go over the components of personalized search and recommender systems and demonstrate the applications of various deep learning techniques along the way.

Search and recommender systems are probably the most prevalent ML powered application across the industry. They share most of the components composition and provide a user a ranked list of items, while there is subtle difference that a search system typically acts passively with a clear user intention in terms of queries and a recommender system acts more proactively.

Deep learning has been wildly successful in solving complex tasks such as image recognition, speech recognition, natural language processing and understanding, machine translation, etc. In the area of personalized recommender systems, deep learning has been showing tremendous impact in recent years.

Search and recommender systems can be staged roughly in three phases: 1. User and query understanding, where a query or a user profile are processed so that the systems can use the processed information to 2. retrieve all the related items (high recall) and 3. rank the items by the order of the most relevance to the user's intent (high precision). Each phase has its unique challenges but deep learning has been ubiquitously pushing beyond the limit.

After walking through the talk, we hope the audience would gain some first-hand experience building a personalized search/recommender system using deep learning techniques.

References

  1. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv:cs.DC/1603.04467Google ScholarGoogle Scholar
  2. 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. Activity ranking in LinkedIn feed. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (08 2014). https://doi.org/10.1145/2623330.2623362Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Deepak Agarwal, Liang Zhang, Bee-Chung Chen, Qi He, Zhenhao Hua, Guy Lebanon, Yiming Ma, Pannagadatta Shivaswamy, Hsiao-Ping Tseng, and Jaewon Yang. 2015. Personalizing Linked In Feed. 1651--1660. https://doi.org/10.1145/ 2783258.2788614Google ScholarGoogle Scholar
  4. Trapit Bansal, David Belanger, and Andrew McCallum. 2016. Ask the gru: Multitask learning for deep text recommendations. In RecSys.Google ScholarGoogle Scholar
  5. Leonid Boytsov, David Novak, Yury Malkov, and Eric Nyberg. 2016. Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search. In CIKM.Google ScholarGoogle Scholar
  6. Christopher Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Gregory Hullender. 2005. Learning to Rank using Gradient Descent. ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning, 89--96. https://doi.org/10.1145/1102351.1102363Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. 785--794. https://doi.org/10.1145/2939672.2939785Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, G.s Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & Deep Learning for Recommender Systems. 7--10. https://doi.org/ 10.1145/2988450.2988454Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Corinna Cortes and Vladimir Vapnik. 1995. Support Vector Network. Machine Learning 20 (09 1995), 273--297. https://doi.org/10.1007/BF00994018Google ScholarGoogle Scholar
  10. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL.Google ScholarGoogle Scholar
  11. Nadia Fawaz, Saurabh Kataria, Benjamin Le, Liang Zhang, and Ganesh Venkataraman. 2017. Deep Learning for Personalized Search and Recommender Systems. In KDD.Google ScholarGoogle Scholar
  12. Homa B. Hashemi, Amir Asiaee, and Reiner Kraft. 2016. Query Intent Detection using Convolutional Neural Networks. In International Conference on Web Search and Data Mining, Workshop on Query Understanding.Google ScholarGoogle Scholar
  13. Baotian Hu, Zhengdong Lu, Hang Li,, and Qingcai Chen. 2014. Convolutional Neural Network Architectures for Matching Natural Language Sentences. In NIPS.Google ScholarGoogle Scholar
  14. LinkedIn. 2020. Deep neural ranking framework with Text understanding. https: //github.com/linkedin/detextGoogle ScholarGoogle Scholar
  15. Bhaskar Mitra, Fernando Diaz,, and Nick Craswell. 2017. Learning to match using local and distributed representations of text for web search. In WWW.Google ScholarGoogle Scholar
  16. Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. Learning deep structured semantic models for web search using clickthrough data. In WWW.Google ScholarGoogle Scholar
  17. Yangyang Shi, Kaisheng Yao, Le Tian, and Daxin Jiang. 2016. Deep LSTM based feature mapping for query classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.Google ScholarGoogle ScholarCross RefCross Ref
  18. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS.Google ScholarGoogle Scholar
  19. Jun Xu, Xiangnan He, and Hang Li. 2018. Deep Learning for Matching in Search and Recommendation. In SIGIR.Google ScholarGoogle Scholar
  20. Hamed Zamani, Mostafa Dehghani,W. Bruce Croft, Erik Learned-Miller, and Jaap Kamps. 2018. From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing. In CIKM.Google ScholarGoogle Scholar
  21. Hamed Zamani, Bhaskar Mitra, Xia Song, Nick Craswell, and Saurabh Tiwary. 2018. Neural ranking models with multiple document fields. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, and Deepak Agarwal. 2016. GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction. 363--372. https://doi.org/10.1145/2939672.2939684Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
      August 2020
      3664 pages
      ISBN:9781450379984
      DOI:10.1145/3394486

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      • Published: 20 August 2020

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