Abstract
Twitter and LinkedIn are two popular networks each in its territory. Nowadays, people use both of them in order to update their social (Twitter) and professional (LinkedIn) life. However, an information overload problem, caused by the data provided from these two networks separately, troubled many users. Indeed, the main goal of this work is to provide personalized recommendations that satisfy the user’s expectations by exploiting the user generated content on Twitter and LinkedIn. We propose a method of recommending personalized tweet based on user’s information from twitter and LinkedIn simultaneously. Our Final method considers two main elements: keywords extracted from Twitter and LinkedIn. Those extracted from Twitter are filtered by criteria such as hashtags, URL expansion and Tweets similarity. In order to evaluate our framework performance, we applied our system on a set of data collected from Twitter and LinkedIn. The experiments show that the proposed categorization of the elements is successfully important and our method outperforms several baseline methods.
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Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)
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 Knowl. and Data Eng., IEEE Educational Activities Department 17(6), 734–749 (2005)
Bianne Bernard, A.L., Menasri, F., Al-Hajj Mohamad, R., Kermorvant, C., Mokbel, C., Likforman-Sulem, L.: Dynamic and contextual information in hmm modeling for handwritten word recognition. IEEE Trans. Pattern Anal. Mach. Intell., IEEE Educational Activities Department 33(10), 2066–2080 (2011)
Arase, Y., Xie, X., Duan, M., Hara, T., Nishio, S.: A game based approach to assign geographical relevance to web images. In: WWW, pp. 811–820. ACM (2009)
Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 635–644. ACM, New York (2011)
Bischo, K., Firan, C.S., Nejdl, W., Paiu, R.: Can all tags be used for search? In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 193–202. ACM, New York (2008)
Cha, M., Mislove, A., Gummadi, K.P.: A measurement-driven analysis of information propagation in the Flickr social network. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 721–730. ACM, New York (2009)
Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: CHI 2010: Proceedings of the 28th International Conference on Human Factors in Computing Systems, pp. 1185–1194. ACM, New York (2010)
Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 211–220. ACM, New York (2009)
Gupta, M., Li, R., Yin, Z., Han, J.: Survey on social tagging techniques. SIGKDD Explor. Newsl. 12(1), 58–72 (2010)
Helic, D., Trattner, C., Strohmaier, M., Andrews, K.: on the navigability of social tagging systems. In: Proceedings of the 2010 IEEE Second International Conference on Social Computing, SOCIALCOM 2010, pp. 161–168. IEEE Computer Society, Washington, DC (2010)
Hidetoshi, K., Keiji, Y.: GeoVisualRank: a ranking method of geotagged imagesconsidering visual similarity and geo-location proximity. In: Srinivasan, S., Ramamritham, K., Kumar, A., Ravindra, M.P., Bertino, E., Kumar, R. (eds.), pp. 69–70. ACM (2011)
Huang, J., Chen, J., Cai, H., Friedland, R.P., Koubeissi, M.Z., Laidlaw, D.H., Auchus, A.P.: In Diffusion Tensor MRI Tractography reveals altered brainstem fiber connections accompanying agenesis of the corpus callosum (2011)
Jennings, N.R., Sycara, K., Wooldridge, K.: A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems 1(1), 7–38 (1998)
Laniado, D., Mika, P.: Making sense of twitter. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 470–485. Springer, Heidelberg (2010)
Mezghani, M., Zayani, C.A., Amous, I.: and. Gargouri F A user profile modelling using social annotations: a survey. In: Proceedings of the 21st International Conference Companion on World Wide Web, WWW 2012 Companion, pp. 969–976. ACM, New York (2012)
Michelson, M., Macskassy, S.: A Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, AND 2010, pp. 73–80. ACM, New York (2010)
Mokrane, B., Dimitre, K.: Personnalisation de l’information: aperçu de l’état de l’art et définition d’un modèle flexible de profils. In: CORIA, pp. 201–218 (2005)
Moukas, A., Moukas, R., Maes, P.: Amalthaea: An evolving multi-agent information filtering and discovery system for the www (1998)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD 2000, pp. 427–438. ACM, New York (2000)
Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. SIGKDD Explor. Newsl. 10(2), 90–100 (2012)
Sandholm, T., Ung, H.: Real-time, location-aware collaborative filtering of web content. In: Proceedings of the 2011 Workshop on Context-Awareness in Retrieval and Recommendation, CaRR 2011, pp. 14–18. ACM, New York (2011)
Schubert, P.: and. Koch M. The power of personalization: Customer collaboration and virtual communities. In: Proc. Americas Conf. on Information Systems, AMCIS 2002, Dallas, TX, pp. 1953–1965 (2002)
Zhou, L.T.J., Li, M.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 1397–1405. ACM, New York (2011)
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Benzarti, S., Faiz, R. (2015). EgoTR: Personalized Tweets Recommendation Approach. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Intelligent Systems in Cybernetics and Automation Theory. CSOC 2015. Advances in Intelligent Systems and Computing, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-319-18503-3_23
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DOI: https://doi.org/10.1007/978-3-319-18503-3_23
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