Abstract
Ever-increasing quantities of personal data are generated by individuals, knowingly or unconsciously, actively or passively (e.g., bank transactions, geolocations, posts on web forums, physiological measures captured by wearable sensors). Most of the time, this wealth of information is stored, managed, and valorized in isolated systems owned by private companies or organizations. Personal information management systems (PIMS) propose a groundbreaking counterpoint to this trend. They essentially aim at providing to any interested individual the technical means to re-collect, manage, integrate, and valorize his/her own data through a dedicated system that he/she owns and controls. In this vision paper, we consider personal preferences as first-class citizens data structures. We define and motivate the threefold preference elicitation problem in PIMS - elicitation from local personal data, elicitation from group preferences, and elicitation from user interactions. We also identify hard and diverse challenges to tackle (e.g., small data, context acquisition, small-scale recommendation, low computing resources, data privacy) and propose promising research directions. Overall, we hope that this paper uncovers an exciting and fruitful research track.
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Notes
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See, e.g., https://tinyurl.com/mesinfosValue for a large variety of use-cases.
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References
Abiteboul, S., André, B., Kaplan, D.: Managing your digital life. Commun. ACM 58(5), 32–35 (2015)
Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C., Hull, R., Hüllermeier, E., Kimelfeld, B., Libkin, L., Martens, W., Milo, T., Murlak, F., Neven, F., Ortiz, M., Schwentick, T., Stoyanovich, J., Su, J., Suciu, D., Vianu, V., Yi, K.: Research directions for principles of data management (abridged). SIGMOD Rec. 45(4), 5–17 (2017)
Basu Roy, S., Lakshmanan, L.V., Liu, R.: From group recommendations to group formation. In: Proceedings of SIGMOD 2015, pp. 1603–1616 (2015)
Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. 21, 135–191 (2004)
de Montjoye, Y., Wang, S.S., Pentland, A.: On the trusted use of large-scale personal data. IEEE Data Eng. Bull. 35(4), 5–8 (2012)
Estrin, D.: Small data, where N = Me. Commun. ACM 57(4), 32–34 (2014)
Fürnkranz, J., Hüllermeier, E.: Preference learning: an introduction. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference learning, pp. 1–17. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14125-6_1
Holland, S., Ester, M., Kießling, W.: Preference mining: a novel approach on mining user preferences for personalized applications. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 204–216. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39804-2_20
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Allard, T., Bouadi, T., Duguépéroux, J., Sans, V. (2017). From Self-data to Self-preferences: Towards Preference Elicitation in Personal Information Management Systems. In: Guidotti, R., Monreale, A., Pedreschi, D., Abiteboul, S. (eds) Personal Analytics and Privacy. An Individual and Collective Perspective. PAP 2017. Lecture Notes in Computer Science(), vol 10708. Springer, Cham. https://doi.org/10.1007/978-3-319-71970-2_2
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DOI: https://doi.org/10.1007/978-3-319-71970-2_2
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