ABSTRACT
In this paper, we propose a personalized and contextual ranking algorithm implemented on top of the 3A interaction model. The latter is a generic model intended for designing and describing social and collaborative learning platforms integrating Actors, Assets and group Activities (the 3 "A"). The target user's interactions with his/her environment are modeled in a heterogeneous graph. Then, the algorithm is applied to simultaneously rank actors, assets and group activities taking into account the target user and his/her context. As an illustrative application and a preliminary evaluation, we apply the algorithm on data related to the activities carried out in a European Research Project, especially the collaboration between its members through the joint production of deliverables in workpackages.
- Grudin, J. 1988. Why CSCW applications fail: problems in the design and evaluation of organizational interfaces. In Proceedings of the 1988 ACM Conference on Computer-Supported Cooperative Work (Portland, Oregon, United States, September 26 -- 28, 1988). CSCW '88. ACM, New York, NY, 85--93. Google ScholarDigital Library
- El Helou, S., Gillet, D., Salzmann, C., and Rekik, Y. 2009. Software for Sustaining Interaction, Collaboration and Learning in Communities of Practice. In Solutions and Innovations in Web-Based Technologies for Augmented Learning: Improved Platforms, Tools, and Applications, Advances in Web-based Learning (AWBL) Book Series. 300--316.Google Scholar
- Geyer, W., Dugan, C., Millen, D.R, Muller, M., and Freyne, J. 2008. Recommending topics for self-descriptions in online user profiles. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland, October 23 -- 25, 2008). RecSys '08. ACM, New York, NY, 59--66. Google ScholarDigital Library
- Borgatti, S.P., and Cross, R. 2003. A Relational View of Information Seeking and Learning in Social Networks. Manage. Sci. 49, 4 (Apr. 2003), 432--445. Google ScholarDigital Library
- Langville, A.N., and Meyer C.D. 2003. Deeper Inside PageRank. Internet Mathematics, 1(3):335--380.Google ScholarCross Ref
- Page, L., Brin, S., Motwani, R., and Winograd, T. 1998. The PageRank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project.Google Scholar
- White, S. and Smyth, P. 2003. Algorithms for estimating relative importance in networks. In Proceedings of the Ninth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (Washington, D.C., August 24 -- 27, 2003). KDD '03. ACM, New York, NY, 266--275. Google ScholarDigital Library
- Symeonidis, P., Nanopoulos, A., and Manolopoulos, Y. 2008. Tag recommendations based on tensor dimensionality reduction. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland, October 23 -- 25, 2008). RecSys'08. ACM, New York, NY, 43--50. Google ScholarDigital Library
- Hotho, A., Jäschke, R., Schmitz, C., and Stumme G. 2006. FolkRank: A Ranking Algorithm for Folksonomies. In Proceedings of Workshop on Information Retrieval (FGIR) (Germany, 2006).Google Scholar
- Gulli, A., Cataudella, S., and Foschini, L. 2009. TCSocialRank: Ranking the Social Web. In Proceedings of the 6th international Workshop on Algorithms and Models For the Web-Graph (Barcelona, Spain, February 12 -- 13, 2009). Google ScholarDigital Library
- K. Avrachenkov, D. Donato, and N. Litvak, Eds. Lecture Notes In Computer Science, vol. 5427. Springer-Verlag, Berlin, Heidelberg, 143--154.Google Scholar
- Xi, W., Zhang, B., Chen, Z., Lu, Y., Yan, S., Ma, W., and Fox, E.A. 2004. Link fusion: a unified link analysis framework for multi-type interrelated data objects. In Proceedings of the 13th international Conference on World Wide Web (New York, NY, USA, May 17 -- 20, 2004). WWW '04. ACM, New York, NY, 319--327. Google ScholarDigital Library
- Wang, X., Yuan, F., and Qi, L. 2008. Recommendation in Education Portal by Relation Based Importance Ranking. In Proceedings of the 7th international Conference on Advances in Web Based Learning (Jinhua, China, August 20 -- 22, 2008). F. Li, J. Zhao, T. K. Shih, R. Lau, Q. Li, and D. Mcleod, Eds. Lecture Notes In Computer Science, vol. 5145. Springer-Verlag, Berlin, Heidelberg, 39--48. Google ScholarDigital Library
- Li, L., Muller, M. J., Geyer, W., Dugan, C., Brownholtz, B., and Millen, D. R. 2007. Predicting individual priorities of shared activities using support vector machines. In Proceedings of the Sixteenth ACM Conference on Conference on information and Knowledge Management (Lisbon, Portugal, November 06 -- 10, 2007). CIKM '07. ACM, New York, NY, 515--524. Google ScholarDigital Library
Index Terms
- The 3A contextual ranking system: simultaneously recommending actors, assets, and group activities
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