Abstract
Online social networks provide valuable information sources to collect and identify user information and user interests. This work focuses on using information shared on users’ egocentric network to extract user’s interests. We propose to apply a time-aware method into an existing social profile building process, which is one of our previous team contributions. This strategy aims at weighting user’s interests in the social profile according to their temporal relevance (temporal score). The temporal score of an interest is computed by combining the temporal score of information used to extract the interests (computed by taking into account their freshness) with the temporal score of individuals who share the information in the network (computed by taking into account the freshness of the interaction with the user). In this paper, we show results of intensive experiments conducted on scientific publication networks (DBLP/Mendeley) with presenting a parametric study, comparing the effectiveness of our technique with the time-agnostic technique. We study also the impact of the individual temporal score compared to the information temporal score. The experiments show that our proposition outperforms the existing time-agnostic egocentric network-based user profiling process in terms of precision and recall. Furthermore, we found that the individual temporal score has a larger importance than the information temporal score in calculating the final temporal score. This demonstrates that the dynamic links are more important than the dynamic information when using co-author network data to build the social profile.
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Notes
- 1.
T for “Temporal”.
References
Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. Lecture Notes in Computer Science, vol. 4321, pp. 54–89. Springer, Berlin (2007). http://link.springer.com/chapter/10.1007/978-3-540-72079-9_2
Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing temporal dynamics in twitter profiles for personalized recommendations in the social web. In: Proceedings of the 3rd International Web Science Conference, pp. 2:1–2:8, WebSci ‘11, ACM, New York, NY (2011). doi:10.1145/2527031.2527040
Arnaboldi, V., Conti, M., Passarella, A., Dunbar, R.: 2013. Dynamics of personal social relationships in online social networks: a study on Twitter. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 15–26. COSN ‘13, ACM, New York, NY. doi:10.1145/2512938.2512949
Canut, M.-F., On-At, S., Péninou, A., Sèdes, F.: 2015. Time-aware egocentric network-based user profiling. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 569–572, ASONAM ‘15, ACM, New York, NY. doi:10.1145/2808797.2809415
Cabanac, G.: Accuracy of inter-researcher similarity measures based on topical and social clues. Scientometrics. 87(3), 597–620 (2011). doi:10.1007/s11192-011-0358-1
David, C., Zwerdling, N., Guy, I., Ofek-Koifman, S., N. Har’el, I. Ronen, E. Uziel, S. Yogev, S. Chernov. 2009. Personalized social search based on the user’s social network. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1227–1236, CIKM ‘09, ACM, New York, NY. doi:10.1145/1645953.1646109
Tchuente, D., Canut, M.-F., Jessel, N., Peninou, A., Sèdes, F.: A community-based algorithm for deriving users’ profiles from egocentrics networks: experiment on Facebook and DBLP. Soc. Network Anal. Min. 3(3), 667–683 (2013). doi:10.1007/s13278-013-0113-0
Aral, S., Walker, D.: Tie strength, embeddedness, and social influence: a large-scale networked experiment. Manage. Sci. 60(6), 1352–1370 (2014). doi:10.1287/mnsc.2014.1936
Gama, J., I. Žliobaitė, A. Bifet, M. Pechenizkiy, A. Bouchachia. 2014. A survey on concept drift adaptation.” ACM Comput. Surv. 46 (4): 44:1–44:37. doi:10.1145/2523813.
Tsymbal, A.. 2004. The problem of concept drift: definitions and related work. Technical Report, Department of Computer Science, Trinity College, Dublin
Widmer, G., M. Kubat. 1993. Effective learning in dynamic environments by explicit context tracking. In: P.B. Brazdil (ed.) Machine Learning: ECML-93. Lecture Notes in Computer Science, vol. 667, pp. 227–243, Springer, Berlin. http://link.springer.com/chapter/10.1007/3-540-56602-3_139
Mianowska, B., Nguyen, N.T.: Tuning user profiles based on analyzing dynamic preference in document retrieval systems. Multimedia Tools Appl. 65(1), 93–118 (2013). doi:10.1007/s11042-012-1145-6
Sugiyama, K., K. Hatano, M. Yoshikawa. 2004. Adaptive web search based on user profile constructed without any effort from users. In: Proceedings of the 13th International Conference on World Wide Web, pp. 675–684, WWW ‘04, ACM, New York, NY. doi:10.1145/988672.988764
Koren, Y.. 2009. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456, KDD ‘09, ACM, New York, NY. doi:10.1145/1557019.1557072
Maloof, M.A., Michalski, R.S.: Selecting examples for partial memory learning. Mach. Learn. 41(1), 27–52 (2000). doi:10.1023/A:1007661119649
Bennett, P.N., R.W. White, W. Chu, S.T. Dumais, P. Bailey, F. Borisyuk, X. Cui. 2012. Modeling the impact of short- and long-term behavior on search personalization. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 185–194, SIGIR ‘12, ACM, New York, NY. doi:10.1145/2348283.2348312
Tan, B., X. Shen, and C. Zhai. 2006. Mining long-term search history to improve search accuracy. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 718–723, KDD ‘06, ACM, New York, NY. doi:10.1145/1150402.1150493
Li, D., Cao, P., Guo, Y., Lei, M.: Time weight update model based on the memory principle in collaborative filtering. J. Comput. 8(11), 2763–2767 (2013). doi:10.4304/jcp.8.11.2763-2767
Li, L., Zheng, L., Yang, F., Li, T.: Modeling and broadening temporal user interest in personalized news recommendation. Expert Syst. Appl. 41(7), 3168–3177 (2014). doi:10.1016/j.eswa.2013.11.020
Zheng, N., Li, Q.: A recommender system based on tag and time information for social tagging systems. Expert Syst. Appl. 38(4), 4575–4587 (2011). doi:10.1016/j.eswa.2010.09.131
Kacem, A., M. Boughanem, R. Faiz. 2014. Time-sensitive user profile for optimizing search personlization. In: V. Dimitrova, T. Kuflik, D. Chin, F. Ricci, P. Dolog, G.-J. Houben (eds.) User eModeling, Adaptation, and Personalization. Lecture Notes in Computer Science, vol. 8538, pp. 111–121, Springer International Publishing, Basel. http://link.springer.com/chapter/10.1007/978-3-319-08786-3_10
Stattner, E., Collard, M., Vidot, N.: D2SNet: dynamics of diffusion and dynamic human behaviour in social networks. Comput. Hum. Behav. Adv. Hum. Comput. Interact. 29(2), 496–509 (2013). doi:10.1016/j.chb.2012.06.004
Weng, L., J. Ratkiewicz, N. Perra, B. Gonçalves, C. Castillo, F. Bonchi, R. Schifanella, F. Menczer, A. Flammini. 2013. The role of information diffusion in the evolution of social networks. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 356–364, KDD ‘13, ACM, New York, NY. doi:10.1145/2487575.2487607
Gomez Rodriguez M., J. Leskovec, B. Schölkopf. 2013. Structure and dynamics of information pathways in online media. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 23–32, WSDM ‘13, ACM, New York, NY. doi:10.1145/2433396.2433402
Koohy, H., Koohy, B.: A lesson from the ice bucket challenge: using social networks to publicize science. Front. Genet. 5, (2014). doi:10.3389/fgene.2014.00430
Kumar, R., J. Novak, A. Tomkins. 2006. Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 611–617, KDD ‘06, ACM, New York, NY. doi:10.1145/1150402.1150476
Cazabet, R., M. Leguistin, F. Amblard. 2012. Automated community detection on social networks: useful?Efficient?Asking the users. In: Proceedings of the 4th International Workshop on Web Intelligence; Communities, pp. 6:1–6:8, ACM, New York, NY. doi:10.1145/2189736.2189745
Rosvall, M., Bergstrom, C.T.: An information-theoretic framework for resolving community structure in complex networks. Proc. Natl. Acad. Sci. U. S. A. 104(18), 7327–7331 (2007). doi:10.1073/pnas.0611034104
Pollner, P., Palla, G., Vicsek, T.: Parallel clustering with cfinder. Parallel Process. Lett. 22(1), 1240001 (2012). doi:10.1142/S0129626412400014
Friggeri, A., G. Chelius, E. Fleury. 2011. Triangles to capture social cohesion. arXiv:1107.3231 [Physics]. http://arxiv.org/abs/1107.3231
Salton, G., Waldstein, R.K.: Term relevance weights in on-line information retrieval. Info. Process. Manage. 14(1), 29–35 (1978). doi:10.1016/0306-4573(78)90055-9
Shaw, J.A., E.A. Fox, J.A. Shaw, E.A. Fox. 1994. Combination of multiple searches. In: The Second Text REtrieval Conference (TREC-2), pp. 243–252
Ding, Y., Jacob, E.K., Caverlee, J., Fried, M., Zhang, Z.: Profiling social networks: a social tagging perspective. D-Lib Mag. 15(3/4), (2009). doi:10.1045/march2009-ding
Liben-Nowell, D., J. Kleinberg. 2003. The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 556–559, CIKM ‘03, ACM, New York, NY. doi:10.1145/956863.956972
Tylenda, T., R. Angelova, S. Bedathur. 2009. Towards time-aware link prediction in evolving social networks. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis, pp. 9:1–9:10, SNA-KDD ‘09, ACM, New York, NY. doi:10.1145/1731011.1731020.
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Figure 11 represents the result of the parametric study in terms of precision for all users. We plot a graph for each value of the parameter α. For a given graph, each curve represents the precision for a given value of the parameter γ, for all values of the parameter λ, shown in the X-axis. Figure 12 represents the same information in terms of recall. We recall that α represents the proportion of the structural score compared to the semantic score as presented in Eq. (1), λ represents the time decay rate as presented in Eq. (2), and γ represents the proportion of the individual temporal score compared to the information temporal score as presented in Eq. (9). As noted above, the set of points corresponding to λ = 0.0 and γ = 0.0 represents the CoBSP results. Figures 13 and 14 represent the result of the parametric study in terms of precision and recall for 25 selected users that have at least 50% of common keyword between Mendeley and DBLP.
The average precision for all users with the parametric study for parameters α, γ, and λ
The average recall for all users with the parametric study for parameters α, γ, and λ
The average precision for selected users with the parametric study for parameters α, γ, and λ
The recall average for all users with the parametric study for parameters α, γ, and λ
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On-at, S., Quirin, A., Péninou, A., Baptiste-Jessel, N., Canut, MF., Sèdes, F. (2017). A Parametric Study to Construct Time-Aware Social Profiles. In: Missaoui, R., Abdessalem, T., Latapy, M. (eds) Trends in Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-53420-6_2
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