調查社群媒體推薦產品的決定因素

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：66

、訪客IP：52.14.240.57

姓名

陳柔攸(JOU YU CHEN) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

調查社群媒體推薦產品的決定因素
(Investigating deciding factors of product recommendation in social media)

相關論文

★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響	★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究	★ 太陽能光電產業經營績效評估－應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究	★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究	★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例	★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例	★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討	★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究	★ 資料視覺化圖表與議題之關聯

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限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。

摘要(中)

隨著社群網站的日益興起，在網路世界進行交流的人數急速增長，也逐漸成為將產品推薦給使用者的媒介之一。
過去傳統微網誌(Microblog)的產品推薦方式，大多只在乎使用者個人喜好與興趣，卻忽略了其他可能的影響因素，例如使用者對廠商的喜好、產品受歡迎程度、產品類別喜好、廠商名聲及產品上市時間，並且在過去文獻當中也較少同時將上述變數進行研究分析。因此本文擬針對上述五項不同影響因素，去探討其對使用者的偏好是否造成顯著影響進而提升產品推薦的效果。
本文實證結果顯示， Plurk的使用者對於產品的廠商喜好、產品受歡迎程度與產品類別喜好具有顯著影響關係，會去影響到產品頁面的點擊數。此外，本研究先以回歸模型進行顯著性分析，挑選變數。再透過類神經網路來預測使用者對推薦商品的點擊率是否具有穩健性，實證結果發現類神經具有較好的預測效果。回歸分析所挑的變數也的確有很好的預測力。

摘要(英)

With the growing popularity of the social network, the number of people using the social network to communicate and interactive with others increased steadily. As a result, social commerce has become a new phenomena.

In the past, most of the product recommendation in Microblog only deal with personal preferences and interests, and ignores other possible factors such as crowd Interest, Popularity of products, reputation of creators, types of preference and recency. These variables are used by facebook to recommend posts to users. Therefore, this research adapted the five aspects and analyzed their effectiveness to recommend products on social media sites.

The empirical results show that the Interest, Popularity and Type have significant impacts on recommendation effectivness. In addition, this studies also utilized Artificial Neural Networks to predict the click through rates of recommended web pages. The results show that the Artificial Neural Networks have better predictive effect then Linear Regression. However, the three variables identified by Linear Regression indeed outperform the other variables.

關鍵字(中)

★ 社群媒體
★ 推薦系統
★ NewsFeed

關鍵字(英)

★ social media
★ recommendation
★ NewsFeed

論文目次

摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章緒論 1
1-1研究背景與動機 1
1-2研究目的 4
1-3研究架構 4
第二章文獻探討 6
2-1微網誌的推薦 6
2-2推薦方法 8
2-3 社群網站動態演算法應用與改變 10
第三章研究模型 13
3-1社群網站的選擇 13
3-2 變數定義及分析說明 14
3-2-1 演算法改編 14
3-2-2分析方法 16
第四章資料收集與分析 18
4-1系統實作資料準備 18
4-1-1 推薦產品來源 18
4-1-2 系統實作流程 19
4-1-3 操作型變數定義 21
4-2 資料分析結果 26
4-3預測準確度 27
4-3-1 資料收集 27
4-3-2 資料分析結果 27
第五章建立類神經網路 29
5-1 類神經網路分析結果 29
5-3以類神經網路預測未來點擊數 31
第六章結論與未來研究建議 34
6-1研究結論 34
6-2研究限制及未來研究建議 34
參考文獻 36

參考文獻

[1]. Bai, S.-W. (2013). 考量時間因素的微網誌上產品推薦之研究.
[2]. Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, February). Everyone′s an influencer: quantifying influence on twitter. InProceedings of the fourth ACM international conference on Web search and data mining, ACM, 65-74.
[3]. Banerjee, N., Chakraborty, D., Dasgupta, K., Mittal, S., Joshi, A., Nagar, S., ... & Madan, S. (2009, November). User interests in social media sites: an exploration with micro-blogs. In Proceedings of the 18th ACM conference on Information and knowledge management, ACM, 1823-1826.
[4]. Bucher, Taina. "Want to be on the top? Algorithmic power and the threat of invisibility on Facebook." new media & society 14.7 (2012): 1164-1180.
[5]. Chen, Y. L., Cheng, L. C., & Chuang, C. N. (2008). A group recommendation system with consideration of interactions among group members. Expert systems with applications, 34(3), 2082-2090.
[6]. 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.
[7]. Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories.Decision support systems, 42(3), 1872-1888.
[8]. Christensen, I. A., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert Systems with Applications, 38(11), 14127-14135.
[9]. Esparza, S. G., O’Mahony, M. P., & Smyth, B. (2012). Mining the real-time web: a novel approach to product recommendation.Knowledge-Based Systems, 29, 3-11.
[10]. Gillin, P. (2008). Secrets of Social Media Marketing: How to Use Online Conversations and Customer Communities to Turbo-charge Your Business! : Linden Publishing.
[11]. Guo, J., Zhang, P., & Guo, L. (2012). Mining hot topics from Twitter streams. Procedia Computer Science, 9, 2008-2011.
[12]. Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 256-276.
[13]. Huang, Kuo-Kuang, et al. "Adaptive Newsfeed filter system for the social networking site using artificial bee colony optimization." 網際網路技術學刊 17.2 (2016): 205-216.
[14]. 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
[15]. Kim, H., Suh, K. S., & Lee, U. K. (2013). Effects of collaborative online shopping on shopping experience through social and relational perspectives. Information & Management, 50(4), 169-180.
[16]. Kincaid J (2010) EdgeRank: The secret sauce that makes Facebook’s news feed tick. Techcrunch.
[17]. Kiss, C., & Bichler, M. (2008). Identification of influencers—measuring influence in customer networks. Decision Support Systems, 46(1), 233-253.
[18]. Li, Y. M., & Shiu, Y. L. (2012). A diffusion mechanism for social advertising over microblogs. Decision Support Systems,54(1), 9-22.
[19]. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 415-444.
[20]. 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
[21]. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994) “GroupLens: an open architecture for collaborative filtering of Netnews” Proceedings of the CSC
[22]. Retention, and Prices in the Life Insurance Industry.”Journal of Marketing Research 24(Nov 1987):404-411.
[23]. Van den Bulte, C., & Wuyts, S. H. K. (2007). Social networks in marketing. MSI Relevant Knowledge Series.
[24]. Wang, K. Y., Ting, I. H., & Wu, H. J. (2013). Discovering interest groups for marketing in virtual communities: An integrated approach.Journal of Business Research, 66(9), 1360-1366.
[25]. Wang, Y. F., Chuang, Y. L, Hsu, M. H., and Keh, H. C. (2004) “A personalized recommender system for the cosmetic business” Expert Systems with Applications, 26
[26]. Xing, Wenpu, and Ali Ghorbani. "Weighted pagerank algorithm." Communication Networks and Services Research, 2004. Proceedings. Second Annual Conference on. IEEE, 2004.
[27]. 李德治, 數學, & 童惠玲. (2009). 多變量分析: 專題及論文常用的統計方法: 雙葉書廊.
[28]. 聯合新聞網. (2015). 為何百萬鐵粉愛噗浪勝於臉書…. Retrieved May.22, 2015, fromhttps://paste.Plurk.com/show/gFASFBIUNrJQcs4Dc0wb/
[29]. 蕭文龍(2016). 統計分析入門與應用：SPSS中文版+SmartPLS 3 (PLS_SEM)- 碁峰書局
[30]. 類神經網路應用於房地產估價之研究. 住宅學報, 1999, 8: 1-20.
[31]. 陳柏憲, and 唐瓔璋. [顧客關係, 品牌知名度, 企業形象, 服務品質, 顧客滿意度與顧客忠誠度間關係之研究]—以國內醫療器材業為例. Diss. 2009.
[32]. http://www.ithome.com.tw/news/111629
iThome 臉書全球用戶數衝高至18.6億 2017
[33]. https://www.bnext.com.tw/article/40252/BN-2016-07-19-174028-223
數位時代台灣活躍用戶破1800萬人，Facebook鎖定電商發力 2016
[34]. http://www.inboundjournals.com/edgerank-is-dead-facebooks-news-feed-algorithm-factors/
EdgeRank成過去－探討Facebook動態消息演算法
[35]. https://sproutsocial.com/insights/facebook-news-feed-algorithm-guide/
Edgerank: A Guide to the Facebook News Feed Algorithm
[36]. https://www.singlegrain.com/social-media-news/facebooks-news-feed-algorithm/
Facebook’s News Feed
[37]. 謝邦昌、邱志洲，類神經網路分析（Neural Network Analysis），曉園出版社
[38]. 陳建中, 卓淑玲, & 曾榮瑜. (2013). 台灣地區華人情緒與相關心理生理資料庫—專業表演者臉部表情常模資料. Chinese Journal of Psychology, 55(4), 439-454.
[39]. https://cofounderinc.com/2013/04/06/collaborative-filtering/
淺談協同過濾（Collaborative Filtering）

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2017-6-22

推文