Towards persuasive social recommendation: knowledge model

Abstract

[EN] The exponential growth of social networks makes fingerprint let by users on the Internet a great source of information, with data about their preferences, needs, goals, profile and social environment. These data are distributed across di↵erent sources of information (social networks, blogs, databases, etc.) that may contain inconsistencies and their accuracy is uncertain. Paradoxically, this unprecedented availability of heterogeneous data has meant that users have more information available than they actually are able to process and understand to extract useful knowledge from it. Therefore, new tools that help users in their decision-making processes within the network (e.g. which friends to contact with or which products to consume) are needed. In this paper, we show how we have used a graph-based model to extract and model data and transform it in valuable knowledge to develop a persuasive social recommendation system1.This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788).Palanca Cámara, J.; Heras Barberá, SM.; Jorge Cano, J.; Julian Inglada, VJ. (2015). Towards persuasive social recommendation: knowledge model. ACM SIGAPP Applied Computing Review. 15(2):41-49. https://doi.org/10.1145/2815169.2815173S4149152Desel, J., Pernici, B., Weske, M. Mining Social Networks: Uncovering Interaction Patterns in Business Processes.Business Process Management, Berlin, vol. 3080, pp. 244--260 (2004)Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on KDE 17(6) (2005) 734--749X. Zhou, Y. Xu, Y. Li, A. Josang, and C. Cox, "The state-of-the-art in personalized recommender systems for social networking,"Artificial Intelligence Review, vol. 37, no. 2, pp. 119--132, 2012.Ehrig M., "Ontology Alignment: Bridging the Semantic Gap,"Springer, 2007.Euzenat, J. and Shvaiko P., "Ontology matching,"Springer, Heidelberg (DE), 2007.Bleiholder, J., Naumann, F., "Data Fusion,"ACM Computing Surveys, 41(1):1--41, 2008.Halpin, H., Thomson, H., "Special Issue on Identify, Reference and the Web,"Int. Journal on Semantic Web and Information Systems, 4(2):1--72, 2008.I. Robinson, J. Webber, and E. Eifrem,Graph Databases.O'Reilly, 2013.M. Pazzani and D. Billsus,Content-Based Recommendation Systems, ser. LNCS. Springer-Verlag, 2007, vol. 4321, pp. 325--341.J. Schafer, D. Frankowski, J. Herlocker, and S. Sen,Collaborative Filtering Recommender Systems, ser. LNCS. Springer, 2007, v. 4321, pp. 291--324.R. Burke, "Hybrid Recommender Systems: Survey and Experiments,"User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331--370, 2002.C. Chesñevar, A. Maguitman, and M. González,Empowering Recommendation Technologies Through Argumentation.Springer, 2009, pp. 403--422.G. Linden, J. Hong, M. Stonebraker, and M. Guzdial:, "Recommendation Algorithms, Online Privacy and More,"Comm. of the ACM, vol. 52, no. 5, 2009.Khare, Rohit and Çelik, Tantek, "Microformats: a pragmatic path to the semantic web" in15th international conference on World Wide Web, ACM, 2006, pp. 865--866.R. Fogués, J. M. Such, A. Espinosa, and A. Garcia-Fornes. BFF: A tool for eliciting tie strength and user communities in social networking services.Information Systems Frontiers, 16(2), 225--237, 2014.S. Heras, V. Botti, and V. Julián. Argument-based agreements in agent societies.Neurocomputing, doi:10.1016/j.neucom.2011.02.022, 2011.S. Berkovsky, T. Kuflik, and F. Ricci. Mediation of user models for enhanced personalization in recommender systems. InUser Modeling and User-Adapted Interaction, 18(3), 245--286, 2008.I. Cantador, I. Konstas, and J. M. Jose. Categorising social tags to improve folksonomy-based recommendations.Web Semantics: Science, Services and Agents on the World Wide Web, 9(1), 1--15, 2011.I. Guy, N. Zwerdling, I. Ronen, D. Carmel, and E. Uziel. Social media recommendation based on people and tags. InProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 194--201, ACM, 2010.A. Tiroshi, S. Berkovsky, M. A. Kaafar, D. Vallet, and T. Kuflik. Graph-Based Recommendations: Make the Most Out of Social Data. InUser Modeling, Adaptation, and Personalization, pp. 447--458, Springer International Publishing, 2014.J. J. Pazos, A. Fernández, R. P. Díaz. Recommender Systems for the Social Web, Springer Berlin Heidelberg, 2012.M. Ueda, M. Takahata, and S. Nakajima. UserâĂŹs food preference extraction for personalized cooking recipe recommendation.Semantic Personalized Information Management: Retrieval and Recommendation, SPIM, pp. 98--105 2011.I. Mazzotta, F. De Rosis, and V. Carofiglio. Portia: A user-adapted persuasion system in the healthy-eating domain.Intelligent Systems, IEEE, 22(6), 42--51, 2007.A. Said, and A. Bellogín. You are what you eat! tracking health through recipe interactions. InProceedings of the 6th Workshop on Recommender Systems and the Social Web, RSWeb, 2014.J. Freyne, and S. Berkovsky. Intelligent food planning: personalized recipe recommendation. InProceedings of the 15th international conference on Intelligent user interfaces.pp. 321--324, ACM, 2010