Deep Learning for User Interest and Response Prediction in Online Display Advertising

User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming...

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Veröffentlicht in:	Data Science and Engineering Jg. 5; H. 1; S. 12 - 26
Hauptverfasser:	Gharibshah, Zhabiz, Zhu, Xingquan, Hainline, Arthur, Conway, Michael
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020 Springer SpringerOpen
Schlagworte:	Advertising campaigns Advertising executives Algorithm Analysis and Problem Complexity Analysis Artificial Intelligence Campaign Chemistry and Earth Sciences Click prediction Computer Science Data Mining and Knowledge Discovery Database Management Deep learning Display advertising Email marketing LSTM network Machine learning Physics Statistics for Engineering Systems and Data Security New York Taiwan Deep learning Click prediction Display advertising Campaign LSTM network
ISSN:	2364-1185, 2364-1541
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Abstract	User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, LSTM cp and LSTM ip , for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction.
AbstractList	User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, LSTM cp and LSTM ip , for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction. Abstract User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, $${\hbox {LSTM}}_{\mathrm{cp}}$$ LSTM cp and $${\hbox {LSTM}}_{\mathrm{ip}}$$ LSTM ip , for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction. User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, $${\hbox {LSTM}}_{\mathrm{cp}}$$ LSTM cp and $${\hbox {LSTM}}_{\mathrm{ip}}$$ LSTM ip , for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction. User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and actions to the displayed advertisement (Ad). On the other hand, user interests can further help determine the probability of an Ad viewer becoming a buying customer. To date, existing methods for Ad click prediction, or click-through rate prediction, mainly consider representing users as a static feature set and train machine learning classifiers to predict clicks. Such approaches do not consider temporal variance and changes in user behaviors, and solely rely on given features for learning. In this paper, we propose two deep learning-based frameworks, [Formula omitted] and [Formula omitted], for user click prediction and user interest modeling. Our goal is to accurately predict (1) the probability of a user clicking on an Ad and (2) the probability of a user clicking a specific type of Ad campaign. To achieve the goal, we collect page information displayed to the users as a temporal sequence and use long short-term memory (LSTM) network to learn features that represents user interests as latent features. Experiments and comparisons on real-world data show that, compared to existing static set-based approaches, considering sequences and temporal variance of user requests results in improvements in user Ad response prediction and campaign specific user Ad click prediction.
Audience	Academic
Author	Conway, Michael Gharibshah, Zhabiz Hainline, Arthur Zhu, Xingquan
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References	ChapelleOlivierManavogluErenRosalesRomerSimple and Scalable Response Prediction for Display AdvertisingACM Transactions on Intelligent Systems and Technology20145413410.1145/2532128 Su N, He J, Liu Y, Zhang M, Ma S (2018) User intent, behaviour, and perceived satisfaction in product search. In: Proceedings of the eleventh ACM international conference on web search and data mining (WSDM). ACM, New York, NY, USA pp 547–555 Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th international joint conference on artificial intelligence, Melbourne, Australia, August 19–25 Sheil H, Rana O, Reilly R (2018) Predicting purchasing intent: automatic feature learning using recurrent neural networks. arXiv:1807.08207 Deng W, Ling X, Qi Y, Tan T, Manavoglu E, Zhang Q (2018) Ad click prediction in sequence with long short-term memory networks: an externality-aware model. In: The 41st international ACM SIGIR conference on research and development in information retrieval (SIGIR ’18). ACM, New York, NY, USA, pp 1065–1068 Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha, Qatar, pp 1746–1751 Guo T, Zhu X, Wang Y, Chen F (2019) Discriminative sample generation for deep imbalanced learning. In: Proceedings of the international joint conference on artificial intelligence (IJCAI), pp 2406–2412 Li C, Lu Y, Mei Q, Wang D, Pandey S (2015) Click-through prediction for advertising in twitter timeline. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (KDD). ACM, New York, NY, USA, pp 1959–1968 Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019 Liu H, Zhu X, Kalish K, Kayne J (2017) ULTR-CTR: fast page grouping using URL truncation for real-time click through rate estimation. In: Proceedings of the of the IEEE international conference on information reuse and integration (IRI), pp 444–451 Zhu X, Tao H, Wu Z, Cao J, Kalish K, Kayne J (2017) Fraud prevention in online digital advertising. Springer briefs in computer science. ISBN 978-3-319-56792-1, pp 1–51 Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the thirtieth AAAI conference on artificial intelligence (AAAI). AAAI Press, pp 194–200 Zhang Y, Dai H, Xu C, Feng J, Wang T, Bian J, Wang B, Liu T-Y (2014) Sequential click prediction for sponsored search with recurrent neural networks. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence (AAAI). AAAI Press, pp 1369–1375 Richardson M, Dominowska E, Ragno R (2007) Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on world wide web, ACM, New York, NY, USA, pp 521–530 WangXLiWCuiYZhangRMaoJHuaXSClick-through rate estimation for rare events in online advertisingOnline multimedia advertising: techniques and technologies2010HersheyIGI Global112 Feng Y, Lv F, Shen W, Wang M, Sun F, Zhu Y, Yang K (2019) Deep session interest network for click-through rate prediction. In: Sarit Kraus (ed) Proceedings of the 28th international joint conference on artificial intelligence (IJCAI’19). AAAI Press, pp 2301–2307 ZhangDLiYFanJGaoLShenFShenHTProcessing long queries against short text: top-k advertisement matching in news stream applicationsACM TOIS201735328:128:27 IAB (2016) POpenRTB API specification version 2.5. https://www.iab.com/guidelines/real-time-bidding-rtb-project. Accessed 15 Mar 2019 Agarwal D, Agrawal R, Khanna R, Kota N (2010) Estimating rates of rare events with multiple hierarchies through scalable log-linear models. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD). ACM, New York, NY, USA, pp 213–222 Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Zhu X, Gai K (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 33(01) Ormandi R, Yang H, Lu Q (2015) Scalable multidimensional hierarchical Bayesian modeling on spark. In: Wei F, Albert B, Qiang Y, Philip SY (eds) Proceedings of the 4th international conference on big data, streams and heterogeneous source mining: algorithms, systems, programming models and applications (BIGMINE’15), vol 41. JMLR.org, pp 33–48 AvilaCPVijayaMSClick through rate prediction for display advertisementInt J Comput Appl20161361521530 European Commission (2018) 2018 reform EU data protection rules. https://ec.europa.eu/commission/priorities/justice-and-fundamental-rights/data-protection/2018-reform-eu-data-protection-rules_en. Accessed 15 Mar 2019 Rendle S (2010) Factorization machines. In: Proceedings of the 2010 IEEE international conference on data mining (ICDM), December 13–17, pp 995–1000 ZhangDongxiangGuoLongNieLiqiangShaoJieWuSaiShenHeng TaoTargeted Advertising in Public Transportation Systems with Quantitative EvaluationACM Transactions on Information Systems2017353129 Qu Y, Cai H, Ren K, Zhang W, Yu Y, Wen Y, Wang J (2016) Product based neural networks for user response prediction. In: IEEE 16th international conference on data mining (ICDM), pp 1149–1154 GharibshahZhabizZhuXingquanHainlineArthurConwayMichaelDeep Learning for Online Display Advertising User Clicks and Interests PredictionWeb and Big Data2019ChamSpringer International Publishing19620410.1007/978-3-030-26072-9_14 Liu Q, Yu F, Wu S, Wang L (2015) A convolutional click prediction model. In: Proceedings of the 24th ACM international on conference on information and knowledge management (CIKM). ACM, New York, NY, USA, pp 1743–1746 Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide and deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems (DLRS 2016). ACM, New York, NY, USA, pp 7–10 IAB (2017) Iab tech lab content taxonomy. https://www.iab.com/guidelines/iab-quality-assurance-guidelines-qag-taxonomy/. Accessed 15 Mar 2019 ZhangLWangSLiuBDeep learning for sentiment analysis : a surveyWiley Interdiscip Rev Data Min Knowl Discov20188e125310.1002/widm.1253 115_CR30 115_CR31 X Wang (115_CR10) 2010 L Zhang (115_CR27) 2018; 8 115_CR14 Olivier Chapelle (115_CR15) 2014; 5 115_CR12 115_CR13 115_CR11 115_CR29 115_CR20 Zhabiz Gharibshah (115_CR7) 2019 115_CR28 115_CR25 115_CR26 115_CR23 115_CR24 115_CR21 115_CR22 Dongxiang Zhang (115_CR16) 2017; 35 115_CR18 115_CR19 115_CR6 115_CR5 115_CR8 D Zhang (115_CR17) 2017; 35 115_CR2 115_CR1 115_CR4 CP Avila (115_CR9) 2016; 136 115_CR3
References_xml	– reference: IAB (2017) Iab tech lab content taxonomy. https://www.iab.com/guidelines/iab-quality-assurance-guidelines-qag-taxonomy/. Accessed 15 Mar 2019 – reference: Li C, Lu Y, Mei Q, Wang D, Pandey S (2015) Click-through prediction for advertising in twitter timeline. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (KDD). ACM, New York, NY, USA, pp 1959–1968 – reference: Richardson M, Dominowska E, Ragno R (2007) Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on world wide web, ACM, New York, NY, USA, pp 521–530 – reference: Agarwal D, Agrawal R, Khanna R, Kota N (2010) Estimating rates of rare events with multiple hierarchies through scalable log-linear models. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD). ACM, New York, NY, USA, pp 213–222 – reference: Liu H, Zhu X, Kalish K, Kayne J (2017) ULTR-CTR: fast page grouping using URL truncation for real-time click through rate estimation. In: Proceedings of the of the IEEE international conference on information reuse and integration (IRI), pp 444–451 – reference: Zhang Y, Dai H, Xu C, Feng J, Wang T, Bian J, Wang B, Liu T-Y (2014) Sequential click prediction for sponsored search with recurrent neural networks. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence (AAAI). AAAI Press, pp 1369–1375 – reference: European Commission (2018) 2018 reform EU data protection rules. https://ec.europa.eu/commission/priorities/justice-and-fundamental-rights/data-protection/2018-reform-eu-data-protection-rules_en. Accessed 15 Mar 2019 – reference: GharibshahZhabizZhuXingquanHainlineArthurConwayMichaelDeep Learning for Online Display Advertising User Clicks and Interests PredictionWeb and Big Data2019ChamSpringer International Publishing19620410.1007/978-3-030-26072-9_14 – reference: Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the thirtieth AAAI conference on artificial intelligence (AAAI). AAAI Press, pp 194–200 – reference: Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha, Qatar, pp 1746–1751 – reference: ZhangLWangSLiuBDeep learning for sentiment analysis : a surveyWiley Interdiscip Rev Data Min Knowl Discov20188e125310.1002/widm.1253 – reference: Rendle S (2010) Factorization machines. In: Proceedings of the 2010 IEEE international conference on data mining (ICDM), December 13–17, pp 995–1000 – reference: Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide and deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems (DLRS 2016). ACM, New York, NY, USA, pp 7–10 – reference: Qu Y, Cai H, Ren K, Zhang W, Yu Y, Wen Y, Wang J (2016) Product based neural networks for user response prediction. In: IEEE 16th international conference on data mining (ICDM), pp 1149–1154 – reference: Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th international joint conference on artificial intelligence, Melbourne, Australia, August 19–25 – reference: IAB (2016) POpenRTB API specification version 2.5. https://www.iab.com/guidelines/real-time-bidding-rtb-project. Accessed 15 Mar 2019 – reference: AvilaCPVijayaMSClick through rate prediction for display advertisementInt J Comput Appl20161361521530 – reference: ZhangDongxiangGuoLongNieLiqiangShaoJieWuSaiShenHeng TaoTargeted Advertising in Public Transportation Systems with Quantitative EvaluationACM Transactions on Information Systems2017353129 – reference: Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Zhu X, Gai K (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 33(01) – reference: ZhangDLiYFanJGaoLShenFShenHTProcessing long queries against short text: top-k advertisement matching in news stream applicationsACM TOIS201735328:128:27 – reference: WangXLiWCuiYZhangRMaoJHuaXSClick-through rate estimation for rare events in online advertisingOnline multimedia advertising: techniques and technologies2010HersheyIGI Global112 – reference: Deng W, Ling X, Qi Y, Tan T, Manavoglu E, Zhang Q (2018) Ad click prediction in sequence with long short-term memory networks: an externality-aware model. In: The 41st international ACM SIGIR conference on research and development in information retrieval (SIGIR ’18). ACM, New York, NY, USA, pp 1065–1068 – reference: Guo T, Zhu X, Wang Y, Chen F (2019) Discriminative sample generation for deep imbalanced learning. In: Proceedings of the international joint conference on artificial intelligence (IJCAI), pp 2406–2412 – reference: Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019 – reference: ChapelleOlivierManavogluErenRosalesRomerSimple and Scalable Response Prediction for Display AdvertisingACM Transactions on Intelligent Systems and Technology20145413410.1145/2532128 – reference: Su N, He J, Liu Y, Zhang M, Ma S (2018) User intent, behaviour, and perceived satisfaction in product search. In: Proceedings of the eleventh ACM international conference on web search and data mining (WSDM). ACM, New York, NY, USA pp 547–555 – reference: Sheil H, Rana O, Reilly R (2018) Predicting purchasing intent: automatic feature learning using recurrent neural networks. arXiv:1807.08207 – reference: Zhu X, Tao H, Wu Z, Cao J, Kalish K, Kayne J (2017) Fraud prevention in online digital advertising. Springer briefs in computer science. ISBN 978-3-319-56792-1, pp 1–51 – reference: Liu Q, Yu F, Wu S, Wang L (2015) A convolutional click prediction model. In: Proceedings of the 24th ACM international on conference on information and knowledge management (CIKM). ACM, New York, NY, USA, pp 1743–1746 – reference: Ormandi R, Yang H, Lu Q (2015) Scalable multidimensional hierarchical Bayesian modeling on spark. In: Wei F, Albert B, Qiang Y, Philip SY (eds) Proceedings of the 4th international conference on big data, streams and heterogeneous source mining: algorithms, systems, programming models and applications (BIGMINE’15), vol 41. JMLR.org, pp 33–48 – reference: Feng Y, Lv F, Shen W, Wang M, Sun F, Zhu Y, Yang K (2019) Deep session interest network for click-through rate prediction. In: Sarit Kraus (ed) Proceedings of the 28th international joint conference on artificial intelligence (IJCAI’19). AAAI Press, pp 2301–2307 – ident: 115_CR25 doi: 10.1145/1835804.1835834 – ident: 115_CR28 doi: 10.24963/ijcai.2019/334 – ident: 115_CR3 doi: 10.1109/IRI.2017.55 – volume: 5 start-page: 1 issue: 4 year: 2014 ident: 115_CR15 publication-title: ACM Transactions on Intelligent Systems and Technology doi: 10.1145/2532128 – ident: 115_CR22 doi: 10.1609/aaai.v33i01.33015941 – ident: 115_CR11 doi: 10.1109/ICDM.2010.127 – ident: 115_CR26 – ident: 115_CR24 – ident: 115_CR21 doi: 10.1609/aaai.v30i1.9971 – ident: 115_CR31 doi: 10.1609/aaai.v28i1.8917 – ident: 115_CR4 – ident: 115_CR2 – volume: 8 start-page: e1253 year: 2018 ident: 115_CR27 publication-title: Wiley Interdiscip Rev Data Min Knowl Discov doi: 10.1002/widm.1253 – ident: 115_CR6 doi: 10.24963/ijcai.2017/239 – start-page: 1 volume-title: Online multimedia advertising: techniques and technologies year: 2010 ident: 115_CR10 – ident: 115_CR12 doi: 10.1109/ICDM.2016.0151 – volume: 136 start-page: 521 issue: 1 year: 2016 ident: 115_CR9 publication-title: Int J Comput Appl – ident: 115_CR13 doi: 10.1145/2806416.2806603 – ident: 115_CR1 doi: 10.1007/978-3-319-56793-8 – ident: 115_CR8 doi: 10.1145/1242572.1242643 – ident: 115_CR14 doi: 10.1145/3209978.3210071 – ident: 115_CR20 – ident: 115_CR5 doi: 10.1145/2783258.2788582 – ident: 115_CR30 doi: 10.3115/v1/D14-1181 – ident: 115_CR29 – volume: 35 start-page: 1 issue: 3 year: 2017 ident: 115_CR16 publication-title: ACM Transactions on Information Systems – ident: 115_CR18 – ident: 115_CR23 doi: 10.24963/ijcai.2019/319 – ident: 115_CR19 doi: 10.1145/3159652.3159714 – volume: 35 start-page: 28:1 issue: 3 year: 2017 ident: 115_CR17 publication-title: ACM TOIS – start-page: 196 volume-title: Web and Big Data year: 2019 ident: 115_CR7 doi: 10.1007/978-3-030-26072-9_14
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Snippet	User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response and... Abstract User interest and behavior modeling is a critical step in online digital advertising. On the one hand, user interests directly impact their response...
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SubjectTerms	Advertising campaigns Advertising executives Algorithm Analysis and Problem Complexity Analysis Artificial Intelligence Campaign Chemistry and Earth Sciences Click prediction Computer Science Data Mining and Knowledge Discovery Database Management Deep learning Display advertising Email marketing LSTM network Machine learning Physics Statistics for Engineering Systems and Data Security
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Title	Deep Learning for User Interest and Response Prediction in Online Display Advertising
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