臺灣博碩士論文加值系統

近年來，線上社群網路日益盛行。活躍的使用者每天透過這些網路，花費數小時互動，而在短時間內創造了驚人的資料量。這些大量產生的新資訊，使得使用者往往需要消耗許多時間才能在其中找到感興趣的資訊。當使用者透過行動裝置來瀏覽社群網路時，更加劇了這樣的問題。為了幫助使用者有效率地發掘感興趣的資訊，我們提出了一個全新的推薦方法。這個方法利用了個別使用者回應訊息的行為特性來進行推薦。此外，我們的方法亦被證明可以輕易地拓展到處理使用者興趣隨時間變化的推薦問題。我們研究了當前最受歡迎的線上社群網路上，使用者回應訊息的行為特性，並在實驗中證明了我們提出的方法較該社群網路當前的推薦系統有顯著的進步。

In recent years, online social networks have been dramatically expanded. Active users spend hours communicating with each other via these networks such that an enormous amount of data is created every second.
The tremendous amount of newly created information costs users much time to discover interesting messages from their online social feeds. The problem is even exacerbated if the users access these networks via mobile devices. To help users discover interesting messages efficiently,
in this paper, we propose a new approach to recommend interesting messages for each user by exploiting the user''s response behaviour. The proposed approach is then demonstrated to be easily extended to deal with the temporal recommendation. We investigate the response behaviour on the most popular social network, and the experimental results show that the proposed approach provides obvious improvement over the current online social feeds.

口試委員會審定書 i
Acknowledgments ii
中文摘要 iii
Abstract iv
Contents v
List of Figures vii
List of Tables viii
1 Introduction 1
2 Related Work 5
2.1 Recommendation Systems . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Social Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Methodology 7
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Score Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3 Message Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.4 Influence from Responding User Set . . . . . . . . . . . . . . . . . . . . 12
3.5 Determine Value of p . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.6 Modification for Temporal Recommendation . . . . . . . . . . . . . . . 18
4 Experiments 20
4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.3 Static Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4 Temporal Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.5 Time Efficiency for Online Recommendation . . . . . . . . . . . . . . . 28
5 Conclusion and Future Works 30
Bibliography 31

[1] Anatomy of facebook. https://www.facebook.com/note.php?note_
id=10150388519243859.
[2] Facebook. https://www.facebook.com/.
[3] Plurk. https://www.plurk.com/.
[4] Twitter. https://www.twitter.com/.
[5] Term-weighting approaches in automatic text retrieval. Information Processing and
Management, 24(5):513 – 523, 1988.
[6] A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social
networks. In Proceedings of the 14th ACM SIGKDD International Conference on
Knowledge Discovery and Data mining, pages 7–15, 2008.
[7] C.-m. Au Yeung and T. Iwata. Capturing implicit user influence in online social
sharing. In Proceedings of the 21st ACM conference on Hypertext and hypermedia,
pages 245–254, 2010.
[8] J.-L. Duan, S. Prasad, and J.-W. Huang. Discovering unknown but interesting items
on personal social network. In Proceedings of the 16th PAKDD Pacific-Asia Conference
on Knowledge Discovery and Data Mining, pages 145–156. 2012.
[9] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework
for performing collaborative filtering. In Proceedings of the 22nd ACM SIGIR
Conference on Research and Development in Information Retrieval, pages 230–237,
1999.
[10] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd
ACM SIGIR Conference on Research and Development in Information Retrieval,
pages 50–57, 1999.
[11] H. Kautz, B. Selman, and M. Shah. Referral web: combining social networks and
collaborative filtering. Communications of the ACM, 40(3):63–65, 1997.
[12] L. R. Keith N. Hampton, Lauren Sessions Goulet and K. Purcell. Social networking
sites and our lives. Technical report, Pew Research Center’s Internet & American
Life Project, 2011.
[13] D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through
a social network. In Proceedings of the 9th ACM SIGKDD International Conference
on Knowledge Discovery and Data mining, pages 137–146, 2003.
[14] Y. Koren. Collaborative filtering with temporal dynamics. In Proceedings of the
15th ACM SIGKDD International Conference on Knowledge Discovery and Data
mining, pages 447–456, 2009.
[15] D. Lowe. Object recognition from local scale-invariant features. In The Proceedings
of the 7th IEEE International Conference on Computer Vision, volume 2, pages 1150
–1157, 1999.
[16] H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble.
In Proceedings of the 32nd ACM SIGIR Conference on Research and Development
in Information Retrieval, pages 203–210, 2009.
[17] H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social
regularization. In Proceedings of the 4th ACM WSDM International Conference on
Web Search and Data Mining, pages 287–296, 2011.
[18] P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of the
2007 ACM conference on Recommender systems, pages 17–24, 2007.
[19] N. Nori, D. Bollegala, and M. Ishizuka. Interest Prediction on Multinomial , Time-
Evolving Social Graphs. Proceedings of the Twenty-Second International Joint Conference
on Artificial Intelligence, pages 2507–2512, 2011.
[20] M. Park, J. Jin, and L. Wilson. Fast content-based image retrieval using quasi-gabor
filter and reduction of image feature dimension. In Proceedings of the Fifth IEEE
Southwest Symposium on Image Analysis and Interpretation, pages 178 –182, 2002.
[21] M. Pazzani and D. Billsus. Content-based recommendation systems. In The Adaptive
Web, volume 4321, pages 325–341. 2007.
[22] J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale
networks. In Proceedings of the 15th ACM SIGKDD International Conference on
Knowledge Discovery and Data mining, pages 807–816, 2009.
[23] Q. Wang and Q. Deng. Catching preference drift with initiators in social network.
In Proceedings of the 17th IEEE ICPADS International Conference on Parallel and
Distributed Systems, pages 829 –834, 2011.
[24] L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation
on graphs via long- and short-term preference fusion. In Proceedings
of the 16th ACM SIGKDD International Conference on Knowledge Discovery and
Data mining, pages 723–732, 2010.