長久以來，使用者在社群平台上往往會不經意透露自己所感興趣的事物，包含心情、經驗及關注的議題。本篇論文試圖建立一個動態關鍵字興趣模型，用以計算每個興趣關鍵字對於使用者的影響力分數並進行排序，藉以臆測使用者可能的興趣。本研究結果與人為觀察使用者所發表的訊息進行興趣猜測的一致性高達91%，可謂本研究模型可以模擬類似人為思考模式以協助判別社群使用者興趣。

User tends to reveal what they are interested in on the social networks but not always in a clear way. This paper provides a dynamic interest keyword model to know the influence of each interest aects the user. So that it can identify the possible interests of the user. The results shows 91% accordance that this model is competitive to human guessed interests by observation. This high accordance shows our proposed model is feasible.

1 INTRODUCTION 1
2 RELATED WORK 3
3 Dynamic influence keyword model 6
3.1 Pre-processing 7
3.1.1 Language Filter and POS-Tagging 7
3.1.2 Rule-based Interest Units Extraction 7
3.2 Interest Graph Generation 9
3.2.1 Weights Tuning 10
3.3 User Graph Generation 11
3.4 Interest Identication 12
3.4.1 Interest Mapping Score 12
3.4.2 Frequent Keywords Score 14
3.4.3 Frequent Pairs Score 15
3.4.4 Ranked interest candidates selection 15
4 Experiment 16
4.1 Experimental data 16
4.1.1 Interests 16
4.1.2 Users 18
4.2 Experimental setup 19
4.3 Experimental results and discussion 19
5 Conclusions and future work 22
References 23

[1] F. Bakalov, B. Konig-Ries, A. Nauerz, and M. Welsch. A hybrid approach to identifying user interests in web portals. In Proceedings of the 9th International Conference on Innovative Internet Community Systems, Jena, Germany, June 2009.
[2] H. Bao and E. Y. Chang. Adheat: an in uence-based
diusion model for propagating hints to match ads. In Proceedings of the 19th international conference on World wide web, WWW '10, pages 71{80, New York, NY, USA, 2010. ACM.
[3] Z. Chen, F. Lin, H. Liu, Y. Liu, W.-Y. Ma, and L. Wenyin. User intention modeling in web applications using data mining. World Wide Web, 5(3):181{191, nov 2002.
[4] M. Daoud, L. Tamine, M. Boughanem, and B. Chebaro. Learning implicit user interests using ontology and search history for personalization. In Proceedings of the 2007 international conference on Web information systems engineering, WISE'07, pages 325{336, Berlin, Heidelberg, 2007. Springer-Verlag.
[5] R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, PODS '01, pages 102{113, New York, NY, USA, 2001. ACM.
[6] K. Gimpel, N. Schneider, B. O'Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith. Part-of-speech tagging for twitter: annotation, features, and experiments. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2, HLT '11, pages 42{47, Stroudsburg, PA, USA, 2011.
Association for Computational Linguistics.
[7] J. Hannon, M. Bennett, and B. Smyth. Recommending twitter users to follow using content and collaborative ltering approaches. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pages 199{206, New York, NY, USA, 2010. ACM.
[8] D. Kim, Y. Jo, I.-C. Moon, and A. Oh. Analysis of twitter lists as a potential source for discovering latent characteristics of users. In Workshop on Microblogging at the ACM Conference on Human Factors in Computer Systems. (CHI 2010), 2010.
[9] P. Mylonas, D. Vallet, P. Castells, M. Fern Andez, and Y. Avrithis. Personalized information retrieval based on context and ontological knowledge. Knowl. Eng. Rev., 3(1):73{100, Mar. 2008.
[10] S. Piao and J. Whittle. A feasibility study on extracting twitter users' interests using nlp tools for
serendipitous connections. In Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom), pages 910{915, Oct.
[11] T. Sakaguchi, Y. Akaho, T. Takagi, and T. Shintani. Recommendations in twitter using conceptual fuzzy sets. In Fuzzy Information Processing Society (NAFIPS), 2010 Annual Meeting of the North American, pages 1{6, July.
[12] C. Wang, R. Raina, D. Fong, D. Zhou, J. Han, and G. Badros. Learning relevance from heterogeneous social network and its application in online targeting. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR '11, pages 655{664, New York, NY, USA, 2011. ACM.
[13] Q. Wang and H. Jin. Exploring online social activities for adaptive search personalization. In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10, pages 999{1008, New York, NY, USA, 2010. ACM.
[14] Z. Wen and C.-Y. Lin. On the quality of inferring interests from social neighbors. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pages 373{382, New York, NY, USA, 2010. ACM.
[15] R. W. White, P. Bailey, and L. Chen. Predicting user interests from contextual information. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pages 63{370, New York, NY, USA, 2009. ACM.
[16] Q. Y. Yang ZHANG, Yao WU. Community discovery in twitter based on user interests. Journal of Computational Information Systems, 8(3), 201s2.