博碩士論文 etd-1015116-102219 詳細資訊


[回到前頁查詢結果 | 重新搜尋]

姓名 陳妍萍(Yen-Ping Chen) 電子郵件信箱 E-mail 資料不公開
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 105學年第1學期
論文名稱(中) 在社群網路上以距離和移動速度為基礎的相似度排序方法
論文名稱(英) A Similarity Ranking Method based on Distance and Velocity for Social Networks
檔案
  • etd-1015116-102219.pdf
  • 本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
    請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
    論文使用權限

    紙本論文:5 年後公開 (2021-11-15 公開)

    電子論文:使用者自訂權限:校內 3 年後、校外 5 年後公開

    論文語文/頁數 中文/70
    統計 本論文已被瀏覽 5076 次,被下載 0 次
    摘要(中) 本論文提出在社群網路上以距離和移動速度為基礎的相似度(Similarity)排序方法,除了針對詢問者與朋友對於同一個物品的喜愛程度而產生的相似度排序,我們了解到如果在實際生活上將相似度應用會使相似度的準確度下降,那是因為我們無法得知朋友是否能立即到達詢問的位置且與詢問者一起做共同喜好的事情,我們在以相似度為基礎下考慮朋友和詢問者的距離以及朋友的移動速度,根據以上提到的兩項因素我們設計了MDBS (Modified Distance-based Similarity)演算法,MDBS在相似度為正時我們直接將其值由大到小排序,但是在相似度為負且詢問者與朋友的距離相同時,我們提出一個演算法來修正其排序,在此演算法中,我們將朋友分成兩種,一種為固定的移動速度,另外一種為不同的移動速度,為了驗證我們在固定移動速度以及移動速度不同下的MDBS演算法,我們分析20位朋友在不同相似度、不同距離以及不同移動速度下的排序。從數值分析的結果中,我們可以發現朋友與詢問者的距離以及朋友抵達詢問者的時間改變了詢問者與朋友的相似度排序,不同於以往只是單純以相似度為基礎的排序方法,本論文使用詢問者與朋友間的距離與不同的移動速度來作相似度的排序更能反映出現實生活中的社群網路。
    摘要(英) In this thesis, we propose a novel similarity ranking based on the distance and velocity. In addition to ranking similsrity between query user and friend’s perfrence for the same item.We konw that the accuaacy of similarity will reduce in reality.It is because we can not know whether a friend can immediately reach the location of the inquiry and asked to do with the common interests of things. We consider friend’s distance and velocity based on similarity. According to two factors as mentioned, we designed MDBS (Modified Distance-based Similarity) algorithm. If the similarity is positive, in MDBS, we order it from large to small directly. When the similarity is negative and friends have the same distance, we propose an algorithm to modify the original order. In the algorithm, we divide friends into two categories; fixed speed and different speeds. In order to validate the algorithm, we use 20 friends as an example. In the numerical analysis, we compute the MDBS ranking based on two different situations, i.e., fixed velocity and variable velocity. From the numerical results, we found out that the MDBS ranking can be varied significantly due to different friends’ distance and velocity. Different from the traditional similarity ranking, in this theis, we consider distance between query user and friends and different moving speed of friends,the MDBS ranking can adequately represent the real life of a social network.
    關鍵字(中)
  • 社群網路
  • 排序
  • 相似度
  • 距離
  • 移動速度
  • 關鍵字(英)
  • Distance
  • Similarity
  • Velocity
  • Ranking
  • Social networks
  • 論文目次 致謝……………………. i
    摘要……………………………………………………………………....ii
    Abstract. iii
    目錄......... iv
    圖表目錄 vi
    第一章 導論 1
    1.1 研究動機 1
    1.2 研究方法 1
    1.3 章節介紹 2
    第二章  社群網路上的相似度 3
    2.1 相似度的定義 3
    2.2 詢問者與朋友間的關係 4
    2.3 相關研究 5
    第三章 考慮距離與移動速度的相似度 9
    3.1 系統架構 9
    3.2 MDBS架構 10
    3.2.1 固定移動速度的MDBS 15
    3.2.2 移動速度可能不同的MDBS 29
    第四章 數學分析與結果討論 32
    4.1 參數的設定 32
    4.2 數值分析 33
    4.2.1固定移動速度的MDBS 33
    4.2.2 移動速度不同的MDBS 36
    4.2.3 固定速度與不同速度的MDBS比較 38
    第五章 結論與未來工作 47
    5.1 結論 47
    5.2 遭遇的困難 47
    5.3 未來的工作 47
    Reference 49
    參考文獻 [1] X. Yang, Y. Guo, and Y. Liu ,“ Bayesian-Inference-based Ommendation in Online Social Networks,” IEEE Transactions on Parallel and Distributed and Systems, Vol. 24, No. 4, pp. 642 – 651, April 2013.
    [2] E. Kim, S. Pyo, E. Park, and M. Kim, “An Automatic Recommendation Scheme of TV Program Contents for (IP) TV Personalization,” IEEE Transactions on Broadcasting, Vol. 24, No. 3, pp. 674 – 684, September 2011.
    [3] H. Sun, Z. Zheng, J. Chen, and M. R. Lyu,” Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering,” IEEE Transactions on Services Computing, Vol. 6, pp. 573 - 579, October-December 2013.
    [4] B. McFee, L. Barrington, and G. Lanckriet,” Learning Content Similarity for Music Recommendation,” IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 4, pp. 2207 - 2218, October 2012.

    [5] H. K. Kim, Y. U. Ryu, Y. Cho, and J. K. Kim, “Customer-Driven Content Recommendation Over a Network of Customers,” IEEE Transactions on Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol. 42, pp.48 – 56, January 2012.
    [6] N. Y. Asabere, F. Xia, W. Wang, J. J. P. C. Rodrigues, F. Basso, and J. Ma.” Improving Smart Conference Participation Through Socially Aware Recommendation,” IEEE Transactions on Human- Machine Systems, Vol. 44, No. 1, pp. 689 – 700, October 2014.
    [7] G. Carullo, A. Castiglione, and A. D. Santis§,” Friendship Recommendations in Online Social Networks,” International Conference on Intelligent Networking and Collaborative Systems, Fisciano, Italy, 2014, pp. 42 - 48.
    [8] X. Tang and J. Zhou,” Dynamic Personalized Recommendation on Sparse Data, ” IEEE Computer Society, Vol. 25, Beijing, China, December 2013, pp. 2895 - 2899.
                               
    [9] K. Lee and K. Lee,” Using Dynamically Promoted Experts for Music Recommendation,” IEEE Transactions on Multimedia, Vol. 16, No. 5, pp. 1201 - 1210, August 2014.
    [10] Cai, H. f. Leung, Q. Li, H. Min, J. Tang, and J. Li,” Typicality-Based Collaborative Filtering Recommendation,” IEEE Transactions on Knowledge and Data Engineering, Vol.26, No. 3, pp. 766 - 779, March 2014.
    [11] O. Khalid, M. U. S. Khan, S. U. Khan, and A. Y. Zomaya,” OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks,” IEEE Transactions on Services Computing, Vol. 7, No. 3, pp. 401 - 414, July-September 2014.
    [12] Y. Mo, J. Chen, X. Xie, C. Luo, and L. T. Yang,” Cloud-Based Mobile Multimedia Recommendation System With User Behavior Information,” IEEE Systems Journal, Vol. 8, issue 1, pp. 184 - 193, Wuhan, China, March 2014.
    [13] Q. Feng, G. Zhao, and T. Mei,” Personalized Recommendation Combining User Interest and Social Circle,” IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 7, pp. 1763 – 1777, July 2014.
    [14] J. Huang, X. Q. Cheng, J. Guo, H. W. Shen, and K. Yang, “ Social Recommendation with Interpersonal Influence,” The authors and IOS Press., Zhejiang, China, 2010, pp. 1 – 1.

    [15] M. F. Hornick and P. T. Corp.,” Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem,” IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 8, pp. 1478 – 1490, August 2012.
    [16] S. Jiang, X. Qian, and T. Mei,” Personalized Travel Sequence Recommendation on Multi-Source Big Social Media,” IEEE Transactions on Big Data, Vol. 2, Vol. 2, pp. 43 - 56, March 2016.

    [17] Z. Wang, L. Sun, W. Zhu, S. Yang, H. Li, and D. Wu,” Joint Social and Content Recommendation for User-Generated Videos in Online Social Network, ” IEEE Transactions on Multimedia, Vol. 15, No.3, pp. 698 - 709, April 2013. 
    [18] M. Gjoka, M. Kurant, C. T. Butts, and A. Markopoulou, “ Practical Recommendations on Crawling Online Social Networks,” IEEE Journal on Selected Areas in Communications, Vol. 29, issue 9, pp. 1872 - 1892, Irvine, CA, USA, October 2011.
    [19] Z. Li, J. Lin, K. Salamatian, and G. Xie, “ Social Connections in User-Generated Content Video Systems: Analysis and Recommendation,” IEEE Transactions on Network and Service Management, Vol. 10, No. 1, pp. 70 - 83, March 2013.
    [20] C. H. Chu, W. C. Wu, C. C. Wang, T. S. Chen, and J. J. Chen,” Friend Recommendation for Location-Based Mobile Social Networks,” Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Tainan, Taiwan, July 3-5, 2013, pp. 365 - 370.
    [21] I. Nunes and L. Marinho,” A Personalized Geographic-based Diffusion Model For Location Recommendations in LBSN,” IEEE 9th Latin American Web Congress, Campina Grande, Brazil, Oct. 22-24, 2014, pp. 59 - 67.
    [22] J. D. Zhang, C. Y. Chow,” TICRec: A Probabilistic Framework to Utilize Temporal Influence Correlations for Time-aware Location Recommendations,” IEEE Transactions on Services Computing, Vol. 9, pp. 633 - 646, March 16, 2015.
    [23] C. Y. Chow, D. Zhang, and Yanhua Li,” iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework,” IEEE Transactions on Services Computing, Vol. 8, pp. 701 - 714, June 3, 2014.
    口試委員
  • 李宗南 - 召集委員
  • 周孜燦 - 委員
  • 黃宗傳 - 委員
  • 許蒼嶺 - 指導教授
  • 口試日期 2016-10-06 繳交日期 2016-11-15

    [回到前頁查詢結果 | 重新搜尋]


    如有任何問題請與論文審查小組聯繫