Abstract
Social Web applications such as Twitter and Flickr are widely used services that generate large volumes of usage data. The challenge of modeling the use and the users of such Social Web services based on their data has received a lot of attention in recent years. In this paper, we go a step further and investigate how the Linked Open Data (LOD) cloud can be leveraged as additional knowledge source in user modeling processes that exploit user data from the Social Web. Specifically, we introduce a user modeling framework that utilizes semantic background knowledge from LOD and evaluate it in the area of point of interest (POI) recommendations. For this purpose, we infer user preferences in POIs based on the users’ behavior observed on Twitter and Flickr, combined with referable evidence from the Web of Data. We compare strategies that aggregate knowledge from two LOD sources: GeoNames and DBpedia. The evaluation validates the advantages of our approach; we show that the user modeling quality improves when LOD-based background information is included in the process.
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Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: Proc. of the 28th Int. Conf. on Human Factors in Computing Systems(CHI), pp. 1185–1194. ACM (2010)
Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing User Modeling on Twitter for Personalized News Recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)
Rowe, M., Stankovic, M.: Aligning Tweets with Events: Automation via Semantics. The Semantic Web Journal, Special Issue on Interoperability, Usability, Applicability (2011)
Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. Journal on Semantic Web and Information Systems (IJSWIS) 5(3), 1–22 (2009)
Stankovic, M., Wagner, C., Jovanovic, J., Laublet, P.: Looking for Experts? What can Linked Data do for You? In: Workshop on Linked Data on the Web (LDOW), Raleigh, USA (2010)
Leonardi, E., Abel, F., Heckmann, D., Herder, E., Hidders, J., Houben, G.-J.: A Flexible Rule-Based Method for Interlinking, Integrating, and Enriching User Data. In: Benatallah, B., Casati, F., Kappel, G., Rossi, G. (eds.) ICWE 2010. LNCS, vol. 6189, pp. 322–336. Springer, Heidelberg (2010)
Cano, A.E., Varga, A., Ciravegna, F.: Volatile Classification of Point of Interests based on Social Activity Streams. In: Workshop on Social Data on the Web (SDoW), Bonn, Germany (2011)
Hecht, B., Hong, L., Suh, B., Chi, E.H.: Tweets from Justin Bieber’s Heart: The Dynamics of the ”Location” Field in User Profiles. In: Proc. of Int. Conf. on Human Factors in Computing Systems (CHI), Vancouver, BC, Canada. ACM (2011)
Golbeck, J., Hansen, D.L.: Computing Political Preference among Twitter Followers. In: Proc. of Int. Conf. on Human Factors in Computing Systems (CHI), Vancouver, BC, Canada. ACM (2011)
Pennacchiotti, M., Popescu, A.M.: A Machine Learning Approach to Twitter User Classification. In: Proc. of the 5th Int. AAAI Conf. on Weblogs and Social Media (ICWSM), Barcelona, Spain. AAAI Press (2011)
Hauff, C., Houben, G.-J.: Geo-Location Estimation of Flickr Images: Social Web Based Enrichment. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 85–96. Springer, Heidelberg (2012)
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Abel, F., Hauff, C., Houben, GJ., Tao, K. (2012). Leveraging User Modeling on the Social Web with Linked Data. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds) Web Engineering. ICWE 2012. Lecture Notes in Computer Science, vol 7387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31753-8_31
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DOI: https://doi.org/10.1007/978-3-642-31753-8_31
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