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Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration

Published: 13 August 2017 Publication History

Abstract

As aggregators, online news portals face great challenges in continuously selecting a pool of candidate articles to be shown to their users. Typically, those candidate articles are recommended manually by platform editors from a much larger pool of articles aggregated from multiple sources. Such a hand-pick process is labor intensive and time-consuming. In this paper, we study the editor article selection behavior and propose a learning by demonstration system to automatically select a subset of articles from the large pool. Our data analysis shows that (i) editors' selection criteria are non-explicit, which are less based only on the keywords or topics, but more depend on the quality and attractiveness of the writing from the candidate article, which is hard to capture based on traditional bag-of-words article representation. And (ii) editors' article selection behaviors are dynamic: articles with different data distribution come into the pool everyday and the editors' preference varies, which are driven by some underlying periodic or occasional patterns. To address such problems, we propose a meta-attention model across multiple deep neural nets to (i) automatically catch the editors' underlying selection criteria via the automatic representation learning of each article and its interaction with the meta data and (ii) adaptively capture the change of such criteria via a hybrid attention model. The attention model strategically incorporates multiple prediction models, which are trained in previous days. The system has been deployed in a commercial article feed platform. A 9-day A/B testing has demonstrated the consistent superiority of our proposed model over several strong baselines.

References

[1]
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, and others 2016. TensorFlow: A system for large-scale machine learning Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). Savannah, Georgia, USA.
[2]
Deepak Agarwal and Bee-Chung Chen 2009. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 19--28.
[3]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).
[4]
Marko Balabanović and Yoav Shoham 1997. Fab: content-based, collaborative recommendation. Commun. ACM Vol. 40, 3 (1997), 66--72.
[5]
Trapit Bansal, David Belanger, and Andrew McCallum. 2016. Ask the GRU: Multi-task Learning for Deep Text Recommendations Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 107--114.
[6]
Trapit Bansal, Mrinal Das, and Chiranjib Bhattacharyya. 2015. Content driven user profiling for comment-worthy recommendations of news and blog articles Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 195--202.
[7]
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin 2003. A neural probabilistic language model. Journal of machine learning research Vol. 3, Feb (2003), 1137--1155.
[8]
Zsolt Bitvai and Trevor Cohn 2015. Non-Linear Text Regression with a Deep Convolutional Neural Network. ACL (2). 180--185.
[9]
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez 2013. Recommender systems survey. Knowledge-based systems Vol. 46 (2013), 109--132.
[10]
Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, and Xian-Sheng Hua 2016. Deep CTR Prediction in Display Advertising. In Proceedings of the 2016 ACM on Multimedia Conference. ACM, 811--820.
[11]
Ting Chen, Wei-Li Han, Hai-Dong Wang, Yi-Xun Zhou, Bin Xu, and Bin-Yu Zang. 2007. Content recommendation system based on private dynamic user profile Machine Learning and Cybernetics, 2007 International Conference on, Vol. Vol. 4. IEEE, 2112--2118.
[12]
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, and others 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 7--10.
[13]
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research Vol. 12, Aug (2011), 2493--2537.
[14]
Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun 2017. Very Deep Convolutional Networks for Text Classification European Chapter of the Association for Computational Linguistics EACL'17.
[15]
Prem K Gopalan, Laurent Charlin, and David Blei. 2014. Content-base recommendations with poisson factorization Advances in Neural Information Processing Systems. 3176--3184.
[16]
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 6645--6649.
[17]
Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, and Erel Uziel 2010. Social media recommendation based on people and tags Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, 194--201.
[18]
Sepp Hochreiter and Jürgen Schmidhuber 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.
[19]
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016).
[20]
Yoon Kim 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014).
[21]
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M Rush. 2015. Character-aware neural language models. arXiv preprint arXiv:1508.06615 (2015).
[22]
Yehuda Koren, Robert Bell, Chris Volinsky, and others. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.
[23]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks Advances in neural information processing systems. 1097--1105.
[24]
Ken Lang 1995. Newsweeder: Learning to filter netnews. In Proceedings of the 12th international conference on machine learning. 331--339.
[25]
Yann LeCun, Yoshua Bengio, and others 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, Vol. 3361, 10 (1995), 1995.
[26]
Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextual-bandit approach to personalized news article recommendation Proceedings of the 19th international conference on World wide web. ACM, 661--670.
[27]
Ting-Peng Liang, Hung-Jen Lai, and Yi-Cheng Ku 2006. Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems Vol. 23, 3 (2006), 45--70.
[28]
Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized news recommendation based on click behavior Proceedings of the 15th international conference on Intelligent user interfaces. ACM, 31--40.
[29]
Andrew McCallum, Kamal Nigam, and others 1998. A comparison of event models for naive bayes text classification AAAI-98 workshop on learning for text categorization, Vol. Vol. 752. Citeseer, 41--48.
[30]
Prem Melville, Raymond J Mooney, and Ramadass Nagarajan. 2002. Content-boosted collaborative filtering for improved recommendations Aaai/iaai. 187--192.
[31]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119.
[32]
Raymond J Mooney and Loriene Roy 2000. Content-based book recommending using learning for text categorization Proceedings of the fifth ACM conference on Digital libraries. ACM, 195--204.
[33]
Jeffrey Pennington, Richard Socher, and Christopher D Manning 2014. Glove: Global Vectors for Word Representation. In EMNLP, Vol. Vol. 14. 1532--1543.
[34]
Owen Phelan, Kevin McCarthy, and Barry Smyth. 2009. Using twitter to recommend real-time topical news. Proceedings of the third ACM conference on Recommender systems. ACM, 385--388.
[35]
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang 2016. Product-based neural networks for user response prediction. arXiv preprint arXiv:1611.00144 (2016).
[36]
Quantcast. 2017. buzzfeed.com Traffic. https://www.quantcast.com/buzzfeed.com#trafficCard. (2017).
[37]
Ruslan Salakhutdinov and Andriy Mnih 2007. Probabilistic Matrix Factorization. In Nips, Vol. Vol. 1. 2--1.
[38]
Aliaksei Severyn and Alessandro Moschitti 2015. Learning to rank short text pairs with convolutional deep neural networks Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 373--382.
[39]
Feliz Solomon. 2016. The Owner of This Hot Chinese App Is Seeking a $10 Billion Valuation. http://fortune.com/2016/11/08/china-toutiao-media-tech-uber-weibo-bytedance/. (2016).
[40]
Jeong-Woo Son, A Kim, Seong-Bae Park, and others. 2013. A location-based news article recommendation with explicit localized semantic analysis Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, 293--302.
[41]
Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015. Training very deep networks. In Advances in neural information processing systems. 2377--2385.
[42]
Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton. 2015. Grammar as a foreign language. In Advances in Neural Information Processing Systems. 2773--2781.
[43]
Chong Wang and David M Blei 2011. Collaborative topic modeling for recommending scientific articles Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 448--456.
[44]
Hao Wang, Naiyan Wang, and Dit-Yan Yeung 2015. Collaborative deep learning for recommender systems Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235--1244.
[45]
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C Courville, Ruslan Salakhutdinov, Richard S Zemel, and Yoshua Bengio. 2015. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. ICML, Vol. Vol. 14. 77--81.
[46]
Nianwen Xue and others 2003. Chinese word segmentation as character tagging. Computational Linguistics and Chinese Language Processing, Vol. 8, 1 (2003), 29--48.
[47]
Diyi Yang, Tianqi Chen, Weinan Zhang, Qiuxia Lu, and Yong Yu 2012. Local implicit feedback mining for music recommendation Proceedings of the sixth ACM conference on Recommender systems. ACM, 91--98.
[48]
Liu Yang, Qingyao Ai, Jiafeng Guo, and W Bruce Croft. 2016. aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 287--296.
[49]
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification Proceedings of NAACL-HLT. 1480--1489.
[50]
Qi Zhang, Yeyun Gong, Jindou Wu, Haoran Huang, and Xuanjing Huang 2016natexlabb. Retweet Prediction with Attention-based Deep Neural Network Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 75--84.
[51]
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013. Optimizing top-n collaborative filtering via dynamic negative item sampling Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, 785--788.
[52]
Weinan Zhang, Tianming Du, and Jun Wang 2016natexlaba. Deep learning over multi-field categorical data. European Conference on Information Retrieval. Springer, 45--57.
[53]
Xiang Zhang, Junbo Zhao, and Yann LeCun 2015. Character-level convolutional networks for text classification Advances in neural information processing systems. 649--657.
[54]
Xiaoxue Zhao, Weinan Zhang, and Jun Wang 2013. Interactive collaborative filtering. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 1411--1420.

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      cover image ACM Conferences
      KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
      August 2017
      2240 pages
      ISBN:9781450348874
      DOI:10.1145/3097983
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      Published: 13 August 2017

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      Author Tags

      1. attention models
      2. convolutional neural network
      3. learning by demonstration
      4. recommendation

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      • National Natural Science Foundation of China
      • Shanghai Sailing Program

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      KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
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      • (2024)Transfer learning from rating prediction to Top-k recommendationPLOS ONE10.1371/journal.pone.030024019:3(e0300240)Online publication date: 28-Mar-2024
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