考量時間因素的微網誌上產品推薦之研究

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姓名

白勝文(Sheng-Wen Bai) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

考量時間因素的微網誌上產品推薦之研究
(Recommendations via short messages with time factors)

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摘要(中)

過去傳統微網誌(Microblog)的推薦方式往往忽略消費者喜好及興趣的變化速度。但消費者的喜好可能隨著時間改變而產生變化，過去相關推薦系統研究中少有研究考慮時間因素，導致推薦系統無法在適合時間點，提供適合的產品或服務給予消費者。
本研究提出三種以噗文資料做的推薦方式：Personalized、Expert、Popularity，推薦契合使用者可能感興趣的產品或服務，並比較Interest-Based的Personalized、Expert及Popularity此三種推薦方式，何種方式能夠明顯提高推薦系統的推薦效果。
透過本研究所提出的個人化推薦方式，相較於其他推薦方式，個人化推薦 (Personalized Recommendation)能夠有效表達使用者的個人興趣及喜好，進而提升推薦產品的點擊率，並且點擊率優於其他種推薦方式。另外本研究透過更新使用者的個人化興趣清單方式，驗證到使用者的點擊率可能隨著時間而有所改變，能夠有效找出使用者興趣變化的時間模式，以利未來行銷商隨時掌握使用者的興趣變化。

摘要(英)

Recommendation systems based on microblogs can catch users interest from message typed in social network sites.. Howerve, user preferences may change over time. To the best of our knowledge, no other work has investigated the effect of freshness on blogs when discovering user interest..
The effectivess of of freshness is measured with the user clicking rate on recommendation pages of books which are related to users interest. To compare and constrat the effectinvess of interest discovering methods, this study experimented with three approaches to discover user interest, namely, Personalized、Expert and Popularity. With the Personalized approach, the key words of users in his/her microblog is identified as interested topic. With the expert approach, an expert with 10 year expereince of using microblogs was hired to analyze blogs and idenitfied interests generic to all friends and fans of a robot in Plurk. The Popularity approach is getting user interest from the top sell list of the largest internet book store in Taiwan.
The approach based on personalized interest is the most effective method to catch user inerest. The expereiment also shows that user interest indeed changed with time and the words represnting personalized interest need to be revised every week to keep them effective.

關鍵字(中)

★ 微網誌
★ 文字探勘

關鍵字(英)

★ Interest-Based Recommendation
★ Microblog
★ Text-ming

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 V
表目錄 VI
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究架構 3
第二章文獻探討 4
2-1 社群網路之口碑行銷 4
2-2 微網誌的產品推薦 5
2-3 推薦方法 6
第三章系統設計 10
3-1產生個人化及專家興趣清單 10
3-2產品推薦系統演算法 12
3-3產品廣告點擊率評估 15
3-4序列化時間更新使用者興趣 15
第四章研究實作 17
4-1系統實作資料準備 17
4-1-1 推薦平台選擇 17
4-1-2 推薦產品來源 17
4-2系統實作流程 18
4-3系統實作結果 23
4-4系統實作結果討論 25
第五章結論與未來研究建議 26
5-1結論 26
5-2研究限制與未來研究建議 27
參考文獻 28

參考文獻

[1]. Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research 1(1): 5-17.
[2]. Chieh-Jen Wang, Shuk-Man Cheng, Lung-Hao Lee, Hsin-Hsi Chen,Wen-shen Liu, Pei-Wen Huang and Shih-Peng Lin (2012). NTUSocialRec: An Evaluation Dataset Constructed from Microblogs for Recommendation Applications in Social Networks. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC), pp. 2328-2333.
[3]. Hyea Kyeong Kim, Hee Young Oh, Ja-Chul Gu, Jae Kyeong Kim: Commenders. (2011). A recommendation procedure for online book communities. Electronic Commerce Research and Applications 10(5): 501-509
[4]. I-Hsien Ting, Pei-Shang Chang, Shyue-Liang Wang (2012). Understanding the Features of Users in Microblogs for Social Recommendation Based on Social Networks Analysis. Journal of Universal Computer Science (SCI), Vol. 18, No. 4, pp. 554-576
[5]. Java, A., Song, X., Finin T., and Tseng B. (2007). Why We Twitter: Understanding Microblogging Usage and Communities. In Proceedings of the Joint 9th WEBKDD and 1st SNA-KDD Workshop, pp. 56-65.
[6]. Mangold, W., Miller, F., and Brockway, G. (1999). Word-of-mouth communication in service marketplace. Journal of Service Marketing 13(1): 73-88.
[7]. Miller, C. C. (2009, JUNE 12 ). Dell Says It Has Earned $3 Million From Twitter. Retrieved feb 25, 2013, from http://bits.blogs.nytimes.com/2009/06/12/dell-has-earned-3-million-from-twitter/
[8]. Min, S. H., Han, I. (2005). Recommender systems using support vector machines, Proceeding of International Conference on Web Engineering, pp. 387-93.
[9]. N Banerjee, D Chakraborty, K Dasgupta (2009). User Interests in Social Media Sites: An Exploration with Microblogs.
[10]. Pei-Shan Chang, I-Hsien Ting, and, Shyue-Liang Wang (2011). Towards Social Recommendation System Based on The Data From Microblogs. International Conference on Advances in Social Networks Analysis and Mining.
[11]. Phelan, O., McCarthy, K., & Smyth, B. (2009). Using Twitter to recommend real-time topical news. In Proceedings of the Third ACM Conference on Recommender Systems (RecSys ’09) (pp. 385–388). New York: ACM Press.
[12]. Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., and Riedl,J. (2002). Getting to know you：learning new user preferences in recommender systems, Proceedings of the IUT 02, San Francisco, CA, pp.127-134.
[13]. Smith, Ted, Coyle, James R., Lightfoot, Elizabeth, and Scott, Amy (2007). Reconsidering Models of Influence: The Relationship between Consumer Social Networks and Word-of-Mouth Effectiveness. Journal of Advertising Research 47(4): 387-397.
[14]. Timon, C. & Feng, Li. (2011). Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs. Decision Support Systems 51(1): 190-197.
[15]. Venkatraman, M.P. (1989). Opinion leaders, adopters, and communicative adopters: a role analysis. Psychology and Marketing 6(1): 51-68.
[16]. Yeong, B. C., Yoon, H. C., & Soung, H. K. (2005). Mining changes in customer buying behavior for collaborative recommendations. Expert Systems with Applications, 28, 359-369.
[17]. Yung-Ming Li, Chia-Hao Lin, Cheng-Yang Lai. (2010). Identifying influential reviewers for word-of-mouth marketing. Electronic Commerce Research and Applications 9(4): 294-304.

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2013-2-27

推文