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論文中文名稱:基於矩陣分解於社群網路中APP之推薦 [以論文名稱查詢館藏系統]
論文英文名稱:App Recommendation Based on Matrix Factorization in Community Networks [以論文名稱查詢館藏系統]
英文姓名:Ching-Chen Su
指導教授英文名:Sung-Shun Weng
英文關鍵詞:Alternative Least SquaresRecommendation SystemsMatrix Factorization
論文英文摘要:The issue of Social Networking and Recommendation has been continuously discussed in recent years. With the prevalence of Facebook、Twitter、Google Plus and other social networks, it has been shown that the collaborative filtering approach can improve the recommendation accuracy and relieve the sparsity problem by using the information obtained through social networks.
This study proposes a recommendation method applying to the community networks. The first step is to collect the rating of users and community friends for the APP items. Second, we find common preferences (similarity) among users through the collection of information, and define the similar friends with higher score as data sources. Third, by using alternating least squares, the prediction score is recommended to the target users.
The experimental results show that the outcome recommended by using the approaches of similar friends and matrix factorization is accurate. Moreover, the questionnaire results show that the recommended ones are accordant with user requirements, and users are interested in the recommended outcomes.
論文目次:摘 要 2
誌 謝 4
目 錄 I
圖目錄 III
表目錄 IV
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 4
1.3論文架構 5
第二章 文獻探討 6
2.1網路服務 6
2.1.1社交網路服務(Social Network Service) 6
2.1.2社群網路(Community Networks) 7
2.1.3社群網路的由來 8
2.2 信任 9
2.2.1 信任的定義 9
2.2.2信任圈推論(Trust Circle Inference) 10
2.3.1人口統計過濾法(Demographic Filtering) 11
2.3.2內容導向推薦(Content Filtering Approach) 11
2.3.3協同過濾推薦(Collaborative Filtering Approach) 12
2.3.混合式推薦( Hybrid-Based Approach ) 14
2.4.1隨機梯度遞減法(Stochastic Gradient Descent) 16
2.4.2交錯最小平方法(Alternating Least Square, ALS) 16
第三章 研究方法 18
3.1系統架構 18
3.2 APP推薦流程 20
3.3預測模組 22
3.3.1資料蒐集 22
3.3.2好友相似度 23
3.3.3結合好友圈與矩陣分解 (matrix factorization) 26
3.4推薦模組 27
第四章 實驗設計與結果 28
4.1系統開發環境與測試資料來源 28
4.2評估指標 31
4.3實驗設計 32
4.3.1實驗說明 32
4.3.2實驗資料 32
4.3.3 T值調整好友圈 33
4.3.4矩陣分解預測結果 34
4.3.4推薦結果 35
4.4APP推薦結果評估 38
第五章 結論 43
5.1研究結論與貢獻 43
5.2管理意涵 44
5.3研究限制與未來展望 45
論文參考文獻:1. 林韶娟、陶晓鹏.基於二值信任網絡的推薦算法改進.,計算機應用與軟體,2012,第29卷,第12期,頁次157-160.
2. 賀超波、湯庸、傅城州、沈玉利、石玉強,結合社交網路訊息的協同過濾方法,濟南大學學報,第34卷,第3期, 2013年,頁次243-252
3. 廖崇勛,結合知識分享與社交網路探索的書籤推薦系統設計,碩士論文,國立高雄第一科技大學資訊管理研究所,高雄,2012。
4. Adomavicius, G., & Tuzhilin, E. "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, Vol. 17, No. 6, 2005, pp. 734-749.
5. Balabanović, M., & Shoham, Y.. "Fab: content-based, collaborative recommendation.", Communications of the ACM Vol. 40, No 3, 1997, pp. "Recommender systems survey. ", Knowledge-Based Systems, Vol. 46, 2013, pp. 109-132.
6. Breese, J. S. Heckerman, D. & Kadie, C., "Empirical Analysis of Predictive Algorithms for Collaborative Filtering. ", In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998, pp.43-52.
7. Burke, R. "Hybrid recommender systems: survey and experiments", User Modeling and User-Adapted Interaction, Vol. 12, No.4, 2002, pp. 331-370.
8. Dou, Y., Niculescu, M. F. & Wu, D. J., "Engineering Optimal Network Effects via Social Media Features and Seeding in Markets for Digital Goods and Servies.", Information Systems Research, Vol. 24, No.1, 2013, pp. 164-185.
9. Golbeck, J. & Parsia, B., "Trust network-based filtering of aggregated claims.", International Journal of Metadata, Semantics and Ontologies, Vol.1, No.1, 2006, pp. 58-65.
10. Haythommthwaite, K., "Characterized social networks as having the following components: Actors. ", New York: Nodes, 2005.
11. Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. "Recommender systems: an introduction. Cambridge University Press.", 2010.
12. Koh, J., & Kim, Y. G. "Sense of virtual community: A conceptual frameworkand empirical validation.", International Journal of Electronic, Vol. 8, No. 2, Winter2003/2004, pp. 75–93.
13. Linden G, & Smith B, & York J, "Amazon.com recommendations: Item to Item collaborative filtering [J] , " IEEE Internet Computing , Vol.7 , No.1, Jan/Feb 2003, pp. 76-80.
14. Liang, T. P., Lai, H. J., & Ku, Y. C. "Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. ", Journal of Management Information Systems, Vol.23, No.3, 2006, pp. 45-70.
15. Li, Q. & Kim, B. M., "Clustering Approach for Hybrid Recommender System. ",IEEE/WIC International Conference on Web Intelligence, 2003, pp. 33-38.
16. Mayer, R.C. Davis, J.H. & Schoorman, F.D., "An Integrative Model of Organizational Trust, Academy of Management Review, " Vol. 20, No. 3, 1995, pp.712
17. Marsden Peter V., Campbell Karen E. "Measuring Tie Strength," Social Forces, Vol. 63, No. 3, 1984, pp. 482-501.
18. Meng X.W., Hu, X., Wang, L.C., & Zhang, Y.J. "Mobile recommender systems and their applications. " Ruan Jian Xue Bao/Journal of Software, 2013, Vol.24, No.1, pp. 91-108.
19. Mitchell, V.W. "Consumer Perceived Risk: Conceptualizations and Models", European Journal of Marketing ,Vol.33, No.1/2, 1999, pp. 163-195.
20. Mills, D. H. (1983). "The Logic and Limits of Trust, " Business and Professional Ethics Journal, Vol.2, No.3, 1983, pp. 77-78.
21. Mooney, R.J. and Roy, L. "Content-based book recommending using learning for text categorization, " Proceedings of the fifth ACM conference on Digital libraries (ACM DL 2000), San Antonio, Texas, June 2-7, pp. 195-204.
22. Montaner, M., López, B., & De La Rosa, J. L. "A taxonomy of recommender agents on the internet., Artificial intelligence review, " Vol.19, No.4, 2003, pp. 285-330.
23. Ozcan, A., & Oguducu, S. "A Recommendation Framework for Mobile Phones Based on Social Network Data," Studies in Computational Intelligence (295), Vol. 20, No. 3, 2010, pp. 139-149.
24. Pazzani, M., "A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review, " Vol. 13, No.5, 1999 , pp.393-408.
25. Rempel, John K.; Ross, Michael; Holmes, John G. "Trust and communicated attributions in close relationships."Journal of Personality and Social Psychology, Vol. 81,No. 1, Jul 2001,pp. 57-64
26. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J."GroupLens: an open architecture for collaborative filtering of netnews. "Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, 1994, pp. 175-186.
27. Rashmi Sinha & Kirsten Swearingen, "Comparing recommendations made by online systems and friends", Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries. 2001.
28. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J., "Item-based collaborative filtering recommendation algorithms. " In: Proceedings of the 10th international conference on World Wide Web. ACM, 2001. pp. 285-295.
29. Scott, J., "Social Network:Critical Concepts in Sociology, "New York, NY: Routledge, 2002.
30. Milgram, S."The small world phenomenon. Psychology Today, " Vol.1, No. 61, 1967.
31. Tiffany A. Pempek, & Yevdokiya A. Yermolayeva, & Sandra L. Calvert
"College students' social networking experiences on Facebook, "Journal of Applied Developmental Psychology, " Vol. 30, No. 3, May–June 2009, pp. 227–238.
32. Wang LC, Meng XW, Zhang YJ. "Context-Aware recommender systems. Ruan Jian Xue Bao/Journal of Software, " Vol. 23,No. 1, 2012,pp.1-20
33. Yang, X., Steck, H., & Liu, Y. "Circle-based Recommendation in Online Social Networks." 2012.
34. Yehuda K, & Robert B, & Chris V , "Matrix Factorization Techniques for Recommender Systems, " IEEE Computer In Computer , Vol. 42 , No. 8, 01 Aug 2009, pp. 20-37.
35. Zhou, Y., Wilkinson, D., Schreiber, R., & Pan, R. "Large-scale parallel collaborative filtering for the netflix prize," In Algorithmic Aspects in Information and Management , Springer Berlin Heidelberg, 2008. pp. 337-348.
36. "2014臺灣消費者行動裝置暨APP使用行為研究調查報告", FIND 資策會,2014(http://www.find.org.tw/market_info.aspx?k=2&n_ID=8304)。
37. "2011年12月社交網路(Social Network)的定義與發展",中華創業網,(http://baodao.weebly.com/1/post/2011/12/-social-network.html)


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