Abstract
Context-awareness is an essential requirement in crafting recommender systems that provide serendipity, i.e. “pleasant surprises”, independently of human command. These solutions must be able to infer interactions based on data from sensors and recognised activities in order to infer what is useful information and when to deliver it. For that, we are devising advanced models of context inference based on the analysis of users’ signals during everyday activities. In this paper, we present a proof-of-concept platform that allows for the application of techniques of deep learning and context analytics to derive patterns in spatio-temporal context signals. We call this composition Big Context. We argue that by understanding how people and things are connected, one can devise novel forms of interactions that provide a more pleasant user experience. In this work, we introduce our method and platform, and illustrate some of the possible techniques using a prototype application that provides serendipitous recommendations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Choi, K.-S., Kim, Y.-B.: Knowledge Seeking Activities for Content Intelligence. In: Sembok, T.M.T., Zaman, H.B., Chen, H., Urs, S.R., Myaeng, S.-H. (eds.) ICADL 2003. LNCS, vol. 2911, pp. 415–426. Springer, Heidelberg (2003)
Dey, A.: Understanding and Using Context. Personal Ubiquitous Computing 5, 4–7 (2001)
Elliott, R.J., Aggoun, L., Moore, J.B.: Hidden Markov Models. Springer (1995)
Fischer, Y., Beyerer, J.: Defining dynamic bayesian networks for probabilistic situation assessment. In: Proc. of International Conference on Information Fusion (FUSION), pp. 888–895. IEEE (2012)
Fonteles, A.S., Neto, B.J.A., Maia, M., Viana, W., Andrade, R.M.C.: An Adaptive Context Acquisition Framework to Support Mobile Spatial and Context-Aware Applications. In: Liang, S.H.L., Wang, X., Claramunt, C. (eds.) W2GIS 2013. LNCS, vol. 7820, pp. 100–116. Springer, Heidelberg (2013)
Fox, E.B., Sudderth, E.B., Jordan, M.I., Willsky, A.S.: Sharing features among dynamical systems with beta processes. In: Conference on Neural Information Processing Systems (NIPS 2009), Vancouver BC, Canada, pp. 549–557 (2009)
Hughes, M., Suddereth, E.B.: Nonparametric discovery of activity patterns from video collections. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Providence RI, USA, pp. 25–32 (2012)
Huỳnh, T., Fritz, M., Schiele, B.: Discovery of activity patterns using topic models. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp 2008), pp. 10–19. ACM, Seoul (2008)
Koch, F., Rao, C.: Towards massively personal education through performance evaluation analytics. International Journal of Information and Education Technology 4(4), 297–301 (2014)
Kramer, D., Kocurova, A., Oussena, S., Clark, T., Komisarczuk, P.: An extensible, self contained, layered approach to context acquisition. In: Proceedings of the Third International Workshop on Middleware for Pervasive Mobile and Embedded Computing. ACM (2011)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML), pp. 282–289. Morgan Kaufmann, San Francisco (2001)
López, G., Brena, R.: Probabilistic Situation Modeling from Ambient Sensors in a Health Condition Monitoring System. In: Urzaiz, G., Ochoa, S.F., Bravo, J., Chen, L.L., Oliveira, J. (eds.) UCAmI 2013. LNCS, vol. 8276, pp. 175–182. Springer, Heidelberg (2013)
Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)
Nath, S.: ACE: Exploiting correlation for energy-efficient and continuous context sensing. In: Proc. of MobiSys 2012, pp. 29–42. ACM (2012)
Parate, A., Chiu, M.C., Ganesan, D., Marlin, B.M.: Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing. In: Proc. of MobiSys 2013, pp. 83–96. ACM (2013)
Poppe, R.: A survey on vision-based human action recognition. Image and vision computing 28(6), 976–990 (2010)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–29. Springer (2011)
Vail, D.L., Veloso, M.M., Lafferty, J.D.: Conditional random fields for activity recognition. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1331–1338. ACM (2007)
Wang, A.I., Ahmad, Q.K.: CAMF - context-aware machine learning framework for android. In: Proceedings of the International Conference on Software Engineering and Applications (SEA 2010), IASTED, Article no. 725–003 (2010)
Wang, X., McCallum, A., Wei, X.: Topical n-grams: Phrase and topic discovery, with an application to information retrieval. In: IEEE International Conference on Data Mining (ICDM 2007), pp. 697–702. IEEE, Omaha (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Koster, A., Koch, F., Kim, Y.B. (2014). Serendipitous Recommendation Based on Big Context. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-12027-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12026-3
Online ISBN: 978-3-319-12027-0
eBook Packages: Computer ScienceComputer Science (R0)