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Collaborative Nowcasting for Contextual Recommendation

Published: 11 April 2016 Publication History

Abstract

Mobile digital assistants such as Microsoft Cortana and Google Now currently offer appealing proactive experiences to users, which aim to deliver the right information at the right time. To achieve this goal, it is crucial to precisely predict users' real-time intent. Intent is closely related to context, which includes not only the spatial-temporal information but also users' current activities that can be sensed by mobile devices. The relationship between intent and context is highly dynamic and exhibits chaotic sequential correlation. The context itself is often sparse and heterogeneous. The dynamics and co-movement among contextual signals are also elusive and complicated. Traditional recommendation models cannot directly apply to proactive experiences because they fail to tackle the above challenges. Inspired by the nowcasting practice in meteorology and macroeconomics, we propose an innovative collaborative nowcasting model to effectively resolve these challenges. The proposed model successfully addresses sparsity and heterogeneity of contextual signals. It also effectively models the convoluted correlation within contextual signals and between context and intent. Specifically, the model first extracts collaborative latent factors, which summarize shared temporal structural patterns in contextual signals, and then exploits the collaborative Kalman Filter to generate serially correlated personalized latent factors, which are utilized to monitor each user's real-time intent. Extensive experiments with real-world data sets from a commercial digital assistant demonstrate the effectiveness of the collaborative nowcasting model. The studied problem and model provide inspiring implications for new paradigms of recommendations on mobile intelligent devices.

References

[1]
http://www.google.com/landing/now/.
[2]
http://dev.windows.com/en-us/cortana.
[3]
http://www.apple.com/ios/whats-new/.
[4]
http://glossary.ametsoc.org/wiki/Nowcast.
[5]
G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In Recommender systems handbook, pages 217--253. Springer, 2011.
[6]
M. Banbura, D. Giannone, M. Modugno, and L. Reichlin. Now-casting and the real-time data flow. Handbook of Economic Forecasting, 2013.
[7]
M. M. Ba\'nbura, D. Giannone, and L. Reichlin. Nowcasting. The Oxford Handbook of Economic Forecasting, 2012.
[8]
L. Charlin, R. Ranganath, J. McInerney, and D. M. Blei. Dynamic poisson factorization. In RecSys, pages 155--162, 2015.
[9]
M. Dixon and G. Wiener. Titan: Thunderstorm identification, tracking, analysis, and nowcasting-a radar-based methodology. Journal of Atmospheric and Oceanic Technology, 10(6):785--797, 1993.
[10]
B. Duncan and C. Elkan. Nowcasting with numerous candidate predictors. In ECML PKDD, pages 370--385. 2014.
[11]
D. Giannone, L. Reichlin, and L. Sala. Monetary policy in real time. In NBER Macroeconomics Annual 2004, Volume 19, pages 161--224. 2005.
[12]
D. Giannone, L. Reichlin, and D. Small. Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4):665--676, 2008.
[13]
R. A. Harshman. Parafac2: Mathematical and technical notes. UCLA Working Papers in Phonetics, 22(3044):122215, 1972.
[14]
T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In SIGIR, pages 259--266, 2003.
[15]
D. Jannach, L. Lerche, and M. Jugovac. Adaptation and evaluation of recommendations for short-term shopping goals. In RecSys, pages 211--218, 2015.
[16]
A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In RecSys, pages 79--86, 2010.
[17]
Y. Koren. Collaborative filtering with temporal dynamics. In KDD, pages 447--456, 2009.
[18]
Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender systems handbook, pages 145--186. 2011.
[19]
V. Lampos and N. Cristianini. Nowcasting events from the social web with statistical learning. TIST, 3(4):72, 2012.
[20]
T. Lansdall-Welfare, V. Lampos, and N. Cristianini. Nowcasting the mood of the nation. Significance, 9(4):26--28, 2012.
[21]
L. Lin, M. Ni, Q. He, J. Gao, A. W. Sadek, and T. I. T. I. Director. Modeling the impacts of inclement weather on freeway traffic speed: An exploratory study utilizing social media data. In Transportation Research Board 94th Annual Meeting, 2015.
[22]
Q. Liu, H. Ma, E. Chen, and H. Xiong. A survey of context-aware mobile recommendations. International Journal of Information Technology & Decision Making, 12(01):139--172, 2013.
[23]
A. E. MacDonald, Y. Xie, and R. H. Ware. Diagnosis of three-dimensional water vapor using a gps network. Monthly Weather Review, 130(2):386--397, 2002.
[24]
T. Mahmood, F. Ricci, and A. Venturini. Improving recommendation effectiveness: Adapting a dialogue strategy in online travel planning. Information Technology & Tourism, 11(4):285--302, 2009.
[25]
C. Mass. Nowcasting: The promise of new technologies of communication, modeling, and observation. Bulletin of the American Meteorological Society, 93(6):797--809, 2012.
[26]
C. Mass and C. F. Mass. Nowcasting: The next revolution in weather prediction. Bulletin of the American Meteorological Society, 2011.
[27]
W. R. Moninger, S. G. Benjamin, B. D. Jamison, T. W. Schlatter, T. L. Smith, and E. J. Szoke. Evaluation of regional aircraft observations using tamdar. Weather and Forecasting, 25(2):627--645, 2010.
[28]
S. W. Raudenbush and A. S. Bryk. Hierarchical linear models: Applications and data analysis methods, volume 1. 2002.
[29]
S. Rendle. Factorization machines with libFM. TIST, 3(3):1--22, 2012.
[30]
S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In WWW, pages 811--820, 2010.
[31]
S. L. Scott and H. R. Varian. Predicting the present with bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1--2):4--23, 2014.
[32]
M. Shokouhi and Q. Guo. From queries to cards: Re-ranking proactive card recommendations based on reactive search history. In SIGIR, pages 695--704, 2015.
[33]
J. Z. Sun, D. Parthasarathy, and K. R. Varshney. Collaborative kalman filtering for dynamic matrix factorization. Transactions on Signal Processing, 62(14):3499--3509, 2014.
[34]
J. W. Wilson, N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon. Nowcasting thunderstorms: A status report. Bulletin of the American Meteorological Society, 79(10):2079--2099, 1998.
[35]
Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Adapting boosting for information retrieval measures. Information Retrieval, 13(3):254--270, 2010.
[36]
Y. Zhang, M. Zhang, Y. Zhang, G. Lai, Y. Liu, H. Zhang, and S. Ma. Daily-aware personalized recommendation based on feature-level time series analysis. In WWW, pages 1373--1383, 2015.

Cited By

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  • (2019)Context-Aware Sequential Recommendations withStacked Recurrent Neural NetworksThe World Wide Web Conference10.1145/3308558.3313567(3172-3178)Online publication date: 13-May-2019
  • (2019)CARL: Aggregated Search with Context-Aware Module Embedding Learning2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851716(1-8)Online publication date: Jul-2019
  • (2018)KDGANProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3326943.3327016(783-794)Online publication date: 3-Dec-2018
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Published In

cover image ACM Other conferences
WWW '16: Proceedings of the 25th International Conference on World Wide Web
April 2016
1482 pages
ISBN:9781450341431

Sponsors

  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Author Tags

  1. intent monitoring
  2. nowcasting
  3. recommendation

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  • Research-article

Funding Sources

  • National Natural Science Foundation of China

Conference

WWW '16
Sponsor:
  • IW3C2
WWW '16: 25th International World Wide Web Conference
April 11 - 15, 2016
Québec, Montréal, Canada

Acceptance Rates

WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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  • (2019)Context-Aware Sequential Recommendations withStacked Recurrent Neural NetworksThe World Wide Web Conference10.1145/3308558.3313567(3172-3178)Online publication date: 13-May-2019
  • (2019)CARL: Aggregated Search with Context-Aware Module Embedding Learning2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851716(1-8)Online publication date: Jul-2019
  • (2018)KDGANProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3326943.3327016(783-794)Online publication date: 3-Dec-2018
  • (2018)Market Abnormality Period Detection via Co-movement Attention Model2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8621877(1514-1523)Online publication date: Dec-2018
  • (2018)A Joint Optimization Approach for Personalized Recommendation DiversificationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-93040-4_47(597-609)Online publication date: 17-Jun-2018
  • (2018)Context-Uncertainty-Aware Chatbot Action Selection via Parameterized Auxiliary Reinforcement LearningAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-93034-3_40(500-512)Online publication date: 19-Jun-2018
  • (2017)Search, Mining, and Their Applications on Mobile DevicesACM Transactions on Information Systems10.1145/308666535:4(1-17)Online publication date: 24-Aug-2017
  • (2017)Collaborative Intent Prediction with Real-Time Contextual DataACM Transactions on Information Systems10.1145/304165935:4(1-33)Online publication date: 16-Aug-2017
  • (2017)Collaborative Filtering-Based Recommendation of Online Social VotingIEEE Transactions on Computational Social Systems10.1109/TCSS.2017.26651224:1(1-13)Online publication date: Mar-2017
  • (2017)Serendipity of Sharing: Large-Scale Measurement and Analytics for Device-to-Device (D2D) Content Sharing in Mobile Social Networks2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SAHCN.2017.7964925(1-9)Online publication date: Jun-2017
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