Detecting Product Adoption Intentions via Multiview Deep Learning

References

• Agarwal S, Sureka A (2016) But I did not mean it!—Intent classification of racist posts on Tumblr. Brynielsson J, Johansson F, eds. 2016 Eur. Intelligence Security Informatics Conf. (EISIC) (IEEE, Piscataway, NJ), 124–127.Google Scholar
• Andrew G, Arora R, Bilmes J, Livescu K (2013) Deep canonical correlation analysis. Dasgupta S, McAllester M, eds. Internat. Conf. Machine Learn. (PMLR), 1247–1255.Google Scholar
• Ashkan A, Clarke CLA (2009) Term-based commercial intent analysis. Sanderson M, Zhai CX, Zobel J, Allan J, Aslam JA, eds. Proc. 32nd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (SIGIR) (ACM, New York), 800–801.Google Scholar
• Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J. Machine Learn. Res. 13(February):281–305.Google Scholar
• Bickel S, Scheffer T (2004) Multi-view clustering. Rastogi R, Morik K, Bramer M, Wu X, eds. Proc. 4th IEEE Internat. Conf. Data Mining (ICDM) (IEEE, Piscataway, NJ), 19–26.Google Scholar
• Chen W, Li X, Zeng DD (2017) Modeling fixed odds betting for future event prediction. Management Inform. Systems Quart. 41(2):645–665.Crossref, Google Scholar
• Chen Z, Liu B, Hsu M, Castellanos M, Ghosh R (2013) Identifying intention posts in discussion forums. Vanderwende L, Daumé III H, Kirchhoff K, eds. Proc. 2013 Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Tech. (Association for Computational Linguistics, Stroudsburg, PA), 1041–1050.Google Scholar
• Cheng J, Lo C, Leskovec J (2017) Predicting intent using activity logs: How goal specificity and temporal range affect user behavior. Proc. 26th Internat. Conf. World Wide Web Companion (WWW) (International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland), 593–601.Google Scholar
• Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J. Machine Learn. Res. 12(August):2493–2537.Google Scholar
• Cui C, Mao W, Zheng X, Zeng D (2017) Mining user intents in online interactions: Applying to discussions about medical event on SinaWeibo platform. Chen H, Zeng DD, Karahanna E, Bardhan I, eds. Smart Health, Lecture Notes in Computer Science (Springer, Cham, Switzerland), 177–183.Crossref, Google Scholar
• Dai H (K), Zhao L, Nie Z, Wen JR, Wang L, Li Y (2006) Detecting online commercial intention (OCI). Proc. 15th Internat. Conf. World Wide Web (WWW) (ACM, New York), 829–837.Google Scholar
• Ding X, Liu T, Duan J, Nie JY (2015) Mining user consumption intention from social media using domain adaptive convolutional neural network. Bonet B, Koenig S, eds. Proc. 29th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA).Google Scholar
• Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization (July 1). J. Machine Learn. Res. 12(7):2121–2159.Google Scholar
• Epure EV, Deneckere R, Salinesi C (2017) Analyzing perceived intentions of public health-related communication on Twitter. ten Teije A, Popow C, Holmes JH, Sacchi L, eds. Artificial Intelligence Medicine, Lecture Notes in Computer Science (Springer, Cham, Switzerland), 182–192.Crossref, Google Scholar
• Fang F, Dutta K, Datta A (2014) Domain adaptation for sentiment classification in light of multiple sources. INFORMS J. Comput. 26(3):586–598.Link, Google Scholar
• Fang X, Hu PJH, Li Z, Tsai W (2013) Predicting adoption probabilities in social networks. Inform. Systems Res. 24(1):128–145.Link, Google Scholar
• Fu Y, Liu J, Li X, Xiong H (2018) A multi-label multi-view learning framework for in-app service usage analysis. ACM Trans. Intelligent Systems Tech. 9(4):1–24.Google Scholar
• Fu Y, Wang P, Du J, Wu L, Li X (2019) Efficient region embedding with multi-view spatial networks: A perspective of locality-constrained spatial autocorrelations. Proc. AAAI Conf. Artificial Intelligence 33(01):906–913.Google Scholar
• Goodfellow I, Bengio Y, Courville A (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
• Guo H, Cherry C, Su J (2017a) End-to-end multi-view networks for text classification. Preprint, submitted April 19, 2017, https://arxiv.org/abs/1704.05907.Google Scholar
• Guo X, Wei Q, Chen G, Zhang J, Qiao D (2017b) Extracting representative information on intra-organizational blogging platforms. Management Inform. Systems Quart. 41(4):1105–1127.Crossref, Google Scholar
• Gupta V, Varshney D, Jhamtani H, Kedia D, Karwa S (2014) Identifying purchase intent from social posts. Proc. 8th Internat. AAAI Conf. Weblogs Soc. Media (AAAI Press, Palo Alto, CA).Google Scholar
• He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. Proc. 2015 IEEE Internat. Conf. Comput. Vision (ICCV) (IEEE, Piscataway, NJ), 1026–1034.Google Scholar
• Hollerit B, Kröll M, Strohmaier M (2013) Toward linking buyers and sellers: Detecting commercial intent on Twitter. Proc. 22nd Internat. Conf. World Wide Web Companion (WWW) (ACM, New York), 629–632.Google Scholar
• Joulin A, Grave E, Bojanowski P, Mikolov T (2017) Bag of tricks for efficient text classification. Lapata M, Blunsom P, Koller A, eds. Proc. 15th Conf. Eur. Chapter Assoc. Comput. Linguistics, vol. 2 (Association for Computational Linguistics, Stroudsburg, PA), 427–431.Google Scholar
• KhudaBukhsh AR, Bennett PN, White RW (2015) Building effective query classifiers: A case study in self-harm intent detection. Proc. 24th ACM Internat. Conf. Inform. Knowledge Management (CIKM) (ACM, New York), 1735–1738.Google Scholar
• Kim Y (2014) Convolutional neural networks for sentence classification. Moschitti A, Pang B, Daelemans W, eds. Proc. 2014 Conf. Empirical Methods Natl. Language Processing (EMNLP) (Association for Computational Linguistics, Stroudsburg, PA), 1746–1751.Google Scholar
• Kim HC, Ghahramani Z (2012) Bayesian classifier combination. Lawrence ND, Girolami M, eds. Proc. 15th Internat. Conf. Artificial Intelligence Statist. (PMLR), 619–627.Google Scholar
• Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. Proc. 3rd Internat. Conf. Learn. Representations.Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Pereira F, Burges CJC, Bottou L, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 25 (Curran Associates, Inc., Red Hook, NY), 1097–1105.Google Scholar
• Lai S, Xu L, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. Proc. 29th AAAI Conf. Artificial Intelligence (AAAI) (AAAI Press, Palo Alto, CA), 2267–2273.Google Scholar
• Li X, Wang YY, Acero A (2008) Learning query intent from regularized click graphs. Myaeng S-H, Oard DW, Sebastiani F, Chua T-S, Leong M-K, eds. Proc. 31st Annual Internat. ACM SIGIR Conf. Res. Development Information Retrieval (SIGIR) (ACM, New York), 339–346.Google Scholar
• Li Y, Yang M, Zhang Z (2019) A Survey of multi-view representation learning. IEEE Trans. Knowledge Data Engrg. 31(10):1863–1883.Google Scholar
• Li X, Chen K, Sun SX, Fung T, Wang H, Zeng DD (2016) A commonsense knowledge-enabled textual analysis approach for financial market surveillance. INFORMS J. Comput. 28(2):278–294.Link, Google Scholar
• Long M, Wang J, Cao Y, Sun J, Yu PS (2016) Deep learning of transferable representation for scalable domain adaptation. IEEE Trans. Knowledge Data Engrg. 28(8):2027–2040.Crossref, Google Scholar
• Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 26 (Curran Associates, Red Hook, NY), 3111–3119.Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. (2011) Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12:2825–2830.Google Scholar
• Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. Moschitti A, Pang B, Daelemans W, eds. Proc. 2014 Conf. Empirical Methods Natl. Language Processing (EMNLP) (Association for Computational Linguistics, Stroudsburg, PA), 1532–1543.Google Scholar
• Ramachandram D, Taylor GW (2017) Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Processing Magazine 34(6):96–108.Crossref, Google Scholar
• Salakhutdinov R, Hinton G (2009) Deep Boltzmann machines. van Dyk D, Welling M, eds. Proc. 12th Internat. Conf. Artificial Intelligence Statist. (PMLR), 448–455.Google Scholar
• Severyn A, Moschitti A (2015) Twitter sentiment analysis with deep convolutional neural networks. Proc. 38th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (SIGIR) (ACM, New York), 959–962.Google Scholar
• Socher R, Li FF (2010) Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. 2010 IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 966–973.Google Scholar
• Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. J. Machine Learn. Res. 15(1):1929–1958.Google Scholar
• Su H, Maji S, Kalogerakis E, Learned-Miller E (2015) Multi-view convolutional neural networks for 3D shape recognition. Proc. 2015 IEEE Internat. Conf. Comput. Vision (ICCV) (IEEE Computer Society, Washington, DC), 945–953.Google Scholar
• Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks Machine Learn. 4(2):26–31.Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems (Curran Associates, Red Hook, NY), 5998–6008.Google Scholar
• Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. McCallum A, Roweis S, eds. Proc. 25th Internat. Conf. Machine Learn. (ICML) (ACM, New York), 1096–1103.Google Scholar
• Vinokourov A, Shawe-Taylor J, Cristianini N (2002) Inferring a semantic representation of text via cross-language correlation analysis. Becker S, Thrun S, Obermayer K, eds. Proc. 15th Internat. Conf. Neural Inform. Processing Systems (NIPS) (MIT Press, Cambridge, MA), 1497–1504.Google Scholar
• Vosoughi S, Vijayaraghavan P, Roy D (2016) Tweet2Vec: Learning tweet embeddings using character-level CNN-LSTM encoder-decoder. Proc. 39th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (SIGIR) (ACM, New York), 1041–1044.Google Scholar
• Wang X, Jiang W, Luo Z (2016) Combination of convolutional and recurrent neural network for sentiment analysis of short texts. Matsumoto Y, Prasad R, eds. Proc. COLING 2016 26th Internat. Conf. Comput. Linguistics (Association for Computational Linguistics, Stroudsburg, PA), 2428–2437.Google Scholar
• Wang J, Cong G, Zhao WX, Li X (2015) Mining user intents in Twitter: A semi-supervised approach to inferring intent categories for tweets. Proc. 29th AAAI Conf. Artificial Intelligence (AAAI) (AAAI Press, Palo Alto, CA), 318–324.Google Scholar
• Wang P, Fu Y, Zhang J, Li X, Lin D (2018) Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs. ACM Trans. Intelligent Systems Tech. 9(6):63:1–63:28.Google Scholar
• Xu L, Jiang C, Ren Y, Chen HH (2016) Microblog dimensionality reduction—A deep learning approach. IEEE Trans. Knowledge Data Engrg. 28(7):1779–1789.Crossref, Google Scholar
• Yang Y, Zeng D, Yang Y, Zhang J (2015) Optimal budget allocation across search advertising markets. INFORMS J. Comput. 27(2):285–300.Link, Google Scholar
• Zhan Y, Zhang Z, Okamoto JM, Zeng DD, Leischow SJ (2019) Underage JUUL use patterns: Content analysis of Reddit messages. J. Medical Internet Res. 21(9):e13038.Crossref, Google Scholar
• Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. Proc. 28th Internat. Conf. Neural Inform. Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, eds. Processing Systems (NIPS), vol. 1 (MIT Press, Cambridge, MA), 649–657.Google Scholar
• Zhang C, Fan W, Du N, Yu PS (2016a) Mining user intentions from medical queries: A neural network based heterogeneous jointly modeling approach. Proc. 25th Internat. Conf. World Wide Web (WWW) (International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland), 1373–1384.Google Scholar
• Zhang Z, Chen G, Zhang J, Guo X, Wei Q (2016b) Providing consistent opinions from online reviews: A heuristic stepwise optimization approach. INFORMS J. Comput. 28(2):236–250.Link, Google Scholar
• Zhang D, Liu J, Zhu H, Liu Y, Wang L, Wang P, Xiong H (2019) Job2Vec: Job title benchmarking with collective multi-view representation learning. Proc. 28th ACM Internat. Conf. Inform. Knowledge Management (CIKM) (Association for Computing Machinery, New York), 2763–2771.Google Scholar
• Zhao W, Guan Z, Chen L, He X, Cai D, Wang B, Wang Q (2018) Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans. Knowledge Data Engrg. 30(1):185–197.Crossref, Google Scholar
• Zhu Z, Luo P, Wang X, Tang X (2014) Multi-view perceptron: A deep model for learning face identity and view representations. Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 27 (Curran Associates, Red Hook, NY), 217–225.Google Scholar