博碩士論文 etd-0724108-120542 詳細資訊


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姓名 黃裕欽(Yu-chin Huang) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 96學年第2學期
論文名稱(中) 結合社會網路技術與內容進行文獻資料庫的推薦   
論文名稱(英) Combining Social Networks and Content for Recommendation in a Literature Digital Library
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    摘要(中) 生活在資訊超載的時代,原有的資訊搜尋機制已不敷使用。隨著電子商務的蓬勃發展,利用資訊科技來發掘消費者潛在的需求變得越來越重要。所以各式各樣的推薦技術相繼被提出,而且許多技術已經在網路商店中實際運作。在這些推薦技術當中,以內容推薦方法(content-based recommendation approach)和協同過濾推薦方法(collaborative filtering recommendation approach)是最成功且最常被採用的推薦技術。最近,利用推薦項目的社會網路因素來判斷相關性的社會網路推薦方法(social network-based recommendation approach)已被提出,它在一些情況下表現的比內容推薦方法來的好,而且可以避免推薦的文章具有高度內容相似性卻低品質的問題。因此,我們提出三種結合內容推薦方法及社會網路推薦方法來達到更佳的推薦效果—轉換式(Switching)、比率式(Proportional)、融合式(Fusion)。根據我們的實驗結果,雖然這三種混合的方法在不同情境下各有優劣,但整體而言它們的表現都比單一的推薦方法要好。此外,我們產生了一些與我們收集的文章有高度內容相關的虛構文章,藉由這些虛構文章的加入來驗證每個方法的精確度。實驗結果顯示利用社會網路資訊能有效降低推薦虛構文章的機會。
    摘要(英) Living in an information-overloading age, the original information searching mechanisms are ineffective and impractical. As the e-commerce is more and more popular, using information technology to discover the latent demand of customers becomes an important issue. Hence, a variety of recommendation techniques have been proposed and many of them have been implemented in real systems, mostly in online stores. Among the techniques, the content-based and collaborative filtering approaches are the ones broadly adopted and proved to be successful. Recently, social network-based recommendation approach has been proposed that takes into account the similarities of items with respect to their social closeness. The social network-based approach performs better than content-based approach in some scenarios and it can also avoid recommending articles that have high content similarity to a user’s favorite articles but low quality. Therefore, we propose three hybrid approaches, Switching, Proportional, and Fusion
    that combine content-based and social network-based approaches in order to achieve a better performance. Our experimental result shows that even though the proposed approaches have pros and cons under different scenarios, in general they achieve better performance than individual
    approaches. Besides, we generate some synthetic articles that have close content similarities to articles in our collection to evaluate the fidelity of each approach. The experimental results show that approaches incorporating social network information have lower chance to recommend these faked articles. 
    關鍵字(中)
  • 社會網路推薦方法
  • 推薦系統
  • 社會網路
  • 混合方法
  • 數位圖書館
  • 關鍵字(英)
  • Social network
  • Recommender system
  • Literature digital library
  • Social network-based recommendation approach
  • Hybrid approach
  • 論文目次 CHAPTER 1 - Introduction....................................................... 1
    1.1 Background ........................................................................ 1
    1.2 Motivation............................................................................ 2
    CHAPTER 2 - Literature Review............................................ 4
    2.1 Recommender Systems................................................. 4
    2.1.1 Content-based Recommendation ............................. 5
    2.1.3 Collaborative Filtering................................................... 6
    2.1.3 Hybrid Approaches........................................................ 8
    2.1.4 Task-Focused Approach ............................................. 9
    2.2 Social Network Analysis ................................................. 9
    2.2.1 The Elements of Social Network .............................. 10
    2.2.2 The Structural Properties of Social Network........... .11
    2.2.3 Graph Theory ................................................................ 11
    2.2.4 Ways for Forming Social Networks .......................... 12
    2.3 Social Network-Based Recommendation...................14
    CHAPTER 3 - Hybrid Approaches ...................................... 20
    3.1 Architecture ...................................................................... 20
    3.2 Hybrid Approaches ......................................................... 21
    CHAPTER 4 - Evaluations ................................................... 24
    4.1 Data Collection ................................................................24
    4.1.1 Expanded Articles ....................................................... 25
    4.1.2 Synthetic Articles ........................................................ 25
    4.2 Data Processing ........................................................... 27
    4.2.1 Constructing the Content Similarity between Articles...........27
    4.2.2 Constructing the Social Similarity between Articles ............... 27
    4.3 Experiments and Results ........................................... 28
    4.3.1 Experiment Design ................................................... 28
    4.3.2 Experimental Results ............................................... 29
    4.3.3 Fidelity .......................................................................... 45
    CHAPTER 5 - Conclusion ................................................. 51
    References .......................................................................... 52
    參考文獻 Adamic, L. A., & Adar, E. (2003). Friends and Neighbors on the Web. Social Networks, 25, 211-230.
    Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
    Balabanovic, M., & Shoham, Y. (1997). Fab: Content-Based, Collaborative Recommendation. Communications of the ACM, 40(3), 66-72.
    Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the Fourteenth Annual Conference on
    Uncertainty in Artificial Intelligence, 43-52
    Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370.
    Chang, C. L., Chen, D. Y., & Chung, T. R. (2002). Browsing Newsgroups with A Social Network Analyzer. Proceedings of the Sixth International Conference on Information
    Visualization, 750-755. 
    Counts, S., & Geraci, J. (2005). Incorporating Physical Co-Presence at Events into Digital Social Networking. Proceedings of 2005 International Conference on Human Factors in Computing Systems, 1308-1311.
    Domingos, P., & Richardson, M. (2001). Mining the Network Value of Customers. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 57-66.
    Golbeck, J., & Hendler, J. (2006). FilmTrust: Movie Recommendations using Trust in Web-Based Social Networks. Consumer Communications and Networking Conference 2006, 1, 282-286.
    Gross, J. L., & Yellen, J. (2004). Handbook of Graph Theory. CRC Press. Hanneman, R. A., & Riddle, M. (2005).
    Introduction to Social Network Methods. (http://faculty.ucr.edu/~hanneman/nettext/)
    Herlocker, J. & Konston, J. (2001). Content-Independent Task-Focused Recommendation, IEEE Internet Computing, 5(6), 40-47.
    Kautz, H., Selman, B., & Milewaki, A. (1995). Agent Amplified Communication. Proceedings of the Thirteenth National Conference on Artificial Intelligence.
    Kautz, H., Selman, B., & Shah, M. (1997). Referral Web: Combining Social Networks and Collaborative Filtering. Communications of the ACM, 40(3), 63-65.
    Lam, C. (2004). SNACK: Incorporating Social Network Information in Automated Collaborative Filtering. Proceedings of the 5th ACM Conference on Electronic Commerce, 254-255.
    Liao, Y. (2006). Employing Social Networks for Recommendation in a Literature Digital Library, Unpublished master dissertation, National Sun Yat-sen University.
    Liben-Nowell, D., & Kleinberg, J. (2003). The Link Prediction Problem for Social Networks. Proceedings of the Twelfth International Conference on Information and Knowledge Management, 556-559.
    Linden, G., Smith, B., & York, J. (2003). Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1), 76-80. 
    Massa, P., & Bhattacharjee, B. (2004). Using Trust in Recommending Systems: an Experimental Analysis. Proceedings of iTrust2004 International Conference.
    Matsuo, Y., Tomobe, H., Hasida, K., & Ishizuka, M. (2004). Finding Social Network for Trust Calculation. Proceedings of the 16th European Conference on Artificial Intelligence, 510-514.
    Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., & Riedl, J. (2003). MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System. Proceedings of International Conference on Intelligent User Interfaces. Mobasher, B., Dai, H., Luo, T., Sung, Y., & Zhu, J. (2000). Integrating Web Usage and Content
    Mining for More Effective Personalization. Proceedings of the International Conference on E-Commerce and Web Technologies, 165-176.
    Newman, M. E. J. (2001). Scientific Collaboration Networks. I. Network Construction and Fundamental results. American Physical Society Journals, 64(1), 016131. 
    Newman, M. E. J. (2001). Clustering and Preferential Attachment in Growing Networks. Physical Review, E, 025102.
    Oh, W., Choi, J. N., & Kim, K. (2005). Coauthorship Dynamics and Knowledge Capital: The patterns of Cross-Disciplinary Collaboration in Information System Research. Journal of Management Information Systems, 22(3), 265-292.
    Pazzani, M. (1999). A Framework for Collaborative, Content-Based, and Demographic Filtering. Artificial Intelligence Review, 393-408.
    Pazzani, M., & Billsus, D. (1997). Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning, 27, 313-331.
    Sack, W. (2000). Conversation Map: A Content-Based Usenet Newsgroup browser. Proceedings of the 5th International Conference on Intelligent user interfaces, 233-240.
    Soboroff, I., & Nicholas, C. (1999). Combining Content and Collaborative in Text Filtering. Proceedings of International Joint Conference on Artificial Intelligence Workshop: Machine Learning for Information Filtering.
    Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. Proceedings of the ACM SIGKDD Explorations, 1(2), 12-33.
    Staab, S., Domingos, P., Mika, P., Golbeck, J., Ding, L., Finin, T., et al. (2005). Social Networks Applied. IEEE Intelligent System, 20, 80-93.
    Torres, R., McNee S. M., Abel, M., Konstan, J. A., & Riedl, J. (2004). Enhancing digital libraries with TechLens+. Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries, 228-236.
    Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications. New York: Cambridge University Press. Wei, C., Shaw, M. J., & Easley, R. F. (2002). A Survey of Recommendation Systems in Electronic Commerce. E-Service: New Directions in Theory and Practice.
    Yan, T. W., Jacobsen, M., Garcia-Molina, H., & Dayal, U. (1996). From User Access Patterns to Dynamic Hypertext Linking. Computer Networks, 28(7-11), 1007-1014.
    Yang, Q., Zhang, H. H., & Li, T. (2001). Mining Web Logs for Prediction Models in WWW Caching and Prefetching. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 473-478.
    Yoshikane, F., & Kageura, K. (2004). Comparative Analysis of Coauthorship Networks of Different Domains: The Growth and Change of Networks. Scientometrics, 60(3), 433-444.
    口試委員
  • 魏志平 - 召集委員
  • 楊婉秀 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2007-06-22 繳交日期 2008-07-24

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