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https://ah.lib.nccu.edu.tw/handle/140.119/136926| 題名: | 基於使用者經驗與資訊行為之推薦系統評估 Evaluating Recommendation System Design Based on User Experience and Information Behavior |
作者: | 蘇子崴 Su, Tzu-Wei |
貢獻者: | 李沛錞 Lee, Pei-Chun 蘇子崴 Su, Tzu-Wei |
關鍵詞: | 推薦系統 使用者經驗 資訊行為 科技推力 需求拉力 Recommendation system User experience Information behavior Technology-push Demand-pull |
日期: | 2021 | 上傳時間: | 2-Sep-2021 | 摘要: | 雖然平台可以藉由推薦系統的協助，成功留住使用者，但其間的資訊量非常龐大，不僅考驗著平台對使用者資訊力的影響，對使用者與系統的分析能力也具有一定的挑戰性。因此，本研究旨在以科技推力觀點，分析推薦系統之技術趨勢，並結合需求拉力觀點，進行推薦系統使用者經驗與資訊行為調查，輔以運用個案研究法，進行以下探討：（1）探討推薦系統技術發展趨勢；（2）探討推薦系統之使用者經驗；（3）探討推薦系統之資訊行為，最終提出相關的結論以及結合使用者經驗與資訊行為觀點、企業電子商務經營觀點之推薦系統建議。\n\n研究結果顯示，以科技推力觀點，目前推薦系統技術表現依舊活躍，各國對推薦系統技術逐漸重視，企業間除了引用自家技術，也引用其他產業，輔助自家開發，且技術領域不僅侷限於商業領域，逐漸擴大技術研發領域。結合需求拉力的觀點，從使用者經驗的角度進行評估推薦系統時，可以針對使用者反饋機制、喜好設定以及推薦原因之操作上進行改善，提升使用者自主性，並根據使用者的變化即時回應。在資訊行為的角度則必須納入網際網路資訊交流管道的意見，了解目前流行趨勢，進而推薦使用者，並在產品規格說明上做統一標準化，方便使用者用於產品間的比較，提升使用者對平台的安心度以及信任度。而企業電子商務經營觀點建議推薦系統以使用者為主軸，且不論是在推薦介面、內容以及產品等，需要以簡單即時的方式運作，進一步針對產品本身以及使用者的行為做行銷上的評估，達到企業整體業績的提升。 Although the website succeed in keep users by the assistance of recommendation system, there are too much information between website and system which not only test the influence of the website on the user’s information power, but also challenges the analyze skill of the user and the system. Therefore, the study aims to analyze the technical trends of the recommendation system from the perspective of technology-push, which also combined with the demand-pull perspective to investigate the user experience and information behavior of the recommendation system supplemented by using in-depth interview, to conduct the following discussions: (1)To discuss technology development trend of recommendation system. (2)To discuss user experience of recommendation system. (3)To discuss information behavior of recommendation system according to the research result, provide the viewpoint suggest of recommendation system that combines user experience and information behavior and enterprise e-commerce management.\n\nThe research found out that the current performance of recommendation system technology is still active by viewing of the perspective of technology, and countries are gradually paying more attention to recommendation system as well. enterprises are not only applying recommendation system on their own business but also citi from others to increasing development. The technical field is not limited to the commercial field, gradually expand the field of technology research and development as well. While evaluating the recommendation system from the perspective of user experience with the viewpoint of demand-pull, enterprises could refer to the user`s feedback mechanism, preference settings, and operation to enhance user autonomy and timely response. It is necessary to adopt the opinions of channel of communication from the perspective of information behavior. in addition to understanding the current fashion trend and then recommend it to users, website should standardize the product specifications so that users can easily compare products and improve user’s confidence and trust in the website.\n\nFinally, recommendation system is suggested to take user on the main point from the perspective of enterprise e-commerce management viewpoints, no matter the recommended interface, content, or product, all of them need to operate in a simple and timely way. Furthermore, it can evaluate the product and the behavior of users in marketing so that increase the overall performance. |
參考文獻: | 中文文獻\n卜小蝶（2012）。網路使用者行為研究。圖書館學與資訊科學大辭典。檢自：http://terms.naer.edu.tw/detail/1679205/ （2020-12-01）。\n王玉珍、李宜玫、吳清麟(2019)。青少年優勢力量表之發展研究。教育心理學報，50(3)，503-528。\n阮明淑、梁峻齊（2009）。專利指標發展研究。圖書館學與資訊科學，35(2)。\n邱皓政（2006），量化研究與統計分析－SPSS中文視窗版資料分析範例解析，第三版，台北：五南圖書公司。\n林珊如（2002）。網路使用者特性與資訊行為研究趨勢之探討。Information Studies，17, 35-47。\n林千立、林美珍(2007)。中文版寂寞量表之效度與信度研究－以老年人為例。輔導與諮商學報，29(2)，41-50。\n林淑惠（2020）。富邦媒 四大科技力迎戰雙11。檢自：https://ctee.com.tw/news/tech/357820.html（2021-06-02)\n吳美美（1996）。資訊時代人人需要資訊素養。社教雙月刊。\n吳榮義（2004）。高科技產業與專利──從專利指標觀察產業技術創新變化。大專院校經濟學教師研習營-財政問題與國家經濟建設。\n李政忠（2004）。網路調查所面臨的問題與解決建議。資訊社會研究，(6)，1-24。doi:10.29843/JCCIS.200401.0002\n李銘傑（2008）。網際網路消費者購買前資訊搜尋行為之研究。國立臺北大學企業管理學系碩士學術論文。\n凃心怡（2019）。 關鍵消費意圖預測技術最懂你。工業技術與資訊月刊，332期2019年08月號。\n凌儀玲、傅豐玲、周逸衡（2000）。影響網路使用者上網購物決定因素之比較。In (Vol. 3, pp. 111-125): 中華管理評論。\n陳雅文（1995)。個案研究法。圖書館學與資訊科學大辭典。檢自：http://terms.naer.edu.tw/detail/1681584/（2021-06-15）。\n陳宗天、王俐涵（2018）。推薦系統之研究內涵與主要研究議題。Electronic Commerce Studies, 16(2), 161-188。\n陳達仁（2009）。專利檢索與分析 (Vol. 3): 經濟部智慧財產局。\n陳福安（2001）。新產品開發的知識管理之探討----以運輸交通工具製造業為例。國立中山大學企業管理學系出版論文。\n張燕舞、蘭小筠（2003)。企业战略与竞争分析方法之一——专利分析法。\n許海玲、吳瀟、李曉東、閻保平（2009）。互联网推荐系统比较研究。软件学报，20(2)， 350-362。\n許峻誠（2019）。使用者經驗研究的回顧與展望。資訊社會研究，36，27-37。 doi:10.29843/JCCIS.201901_(36).0003\n富邦媒體科技股份有限公司（2021）。momo。檢自：http://www.fmt.com.tw/。\n楊瀟茵、許正良（2011）。基于消费者信息行为的数据库构建策略研究。图书情报工作，55(11)，43-42。\n資策會產業情報研究所（2020）。【網購大調查系列一】行動下單急追PC呈五五波 行動商務正式成為主流。台北市：財團法人資訊工業策進會。檢自：https://mic.iii.org.tw/news.aspx?id=555\n葉席吟（2015）。生醫材料領域之國際專利趨勢與技術發展分析。\n葉席吟（2019）。人工智慧之全球專利發展趨勢分析。\n维基百科，自由的百科全書(2020)。MOMO購物網。檢自 https://zh.wikipedia.org/w/index.php?title=MOMO%E8%B3%BC%E7%89%A9%E7%B6%B2&oldid=59571396。\n劉崇汎、林瑞堂、許智威、曾新穆、蘇家輝、蕭欽元（2006）。智慧型個人化多媒體推薦系統之建置。數位典藏技術研討會。\n劉建國、周濤、汪秉宏（2009）。个性化推荐系统的研究进展。\n盧志豪（2003）創新來源之研究─技術推力或市場拉力。長榮大學經營管理研究所碩士論文，台南市。 檢自 https://hdl.handle.net/11296/k2wa2p。\n\n外文文獻\nAcs, Z. J., & Audretsch, D. B. (1988). Innovation in large and small firms: an empirical analysis. The American economic review, 678-690.\nAdomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge & Data Engineering(6), 734-749.\nAhmad Wasfi, A. M. (1998). Collecting user access patterns for building user profiles and collaborative filtering. Paper presented at the Proceedings of the 4th international conference on Intelligent user interfaces.\nAhn, J.-w., Brusilovsky, P., Grady, J., He, D., & Syn, S. Y. (2007). Open user profiles for adaptive news systems: help or harm? Paper presented at the Proceedings of the 16th international conference on World Wide Web.\nAmazon Personalize Real-time personalization and recommendation, based on the same technology used at Amazon.com. Retrieved from https://aws.amazon.com/personalize/\nBalabanović, M. (1998). Exploring versus exploiting when learning user models for text recommendation. User modeling and user-adapted interaction, 8(1-2), 71-102.\nBalabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.\nBartlett, M. S. (1951). A further note on tests of significance in factor analysis. British Journal of Statistical Psychology, 4(1), 1-2.\nBasu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. Paper presented at the Aaai/iaai.\nBates, M. J. (2010). Information behavior. Encyclopedia of library and information sciences, 3, 2381-2391.\nBelk, R. W. (1975). Situational variables and consumer behavior. Journal of Consumer Research, 2(3), 157-164.\nBettman, J. R., & Kakkar, P. (1977). Effects of information presentation format on consumer information acquisition strategies. Journal of Consumer Research, 3(4), 233-240.\nBobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132.\nBostandjiev, S., O`Donovan, J., & Höllerer, T. (2012). TasteWeights: a visual interactive hybrid recommender system. Paper presented at the Proceedings of the sixth ACM conference on Recommender systems.\nBreese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence.\nBrem, A., & Voigt, K.-I. (2009). Integration of market pull and technology push in the corporate front end and innovation management—Insights from the German software industry. Technovation, 29(5), 351-367.\nBrugnoli, G. (2009). Connecting the dots of user experience.\nBurke, R. (2000). Knowledge-based recommender systems. Encyclopedia of library and information systems, 69(Supplement 32), 175-186.\nBurke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4), 331-370.\nBurke, R. (2003). Hybrid systems for personalized recommendations. Paper presented at the IJCAI Workshop on Intelligent Techniques for Web Personalization.\nBurke, R. D., Hammond, K. J., & Yound, B. (1997). The FindMe approach to assisted browsing. IEEE Expert, 12(4), 32-40.\nByström, K., & Järvelin, K. (1995). Task complexity affects information seeking and use. Information Processing & Management, 31(2), 191-213.\nCandillier, L., Meyer, F., & Fessant, F. (2008). Designing specific weighted similarity measures to improve collaborative filtering systems. Paper presented at the Industrial Conference on Data Mining.\nCapon, N., & Burke, M. (1980). Individual, product class, and task-related factors in consumer information processing. Journal of Consumer Research, 7(3), 314-326.\nCapon, N., & Kuhn, D. (1980). A developmental study of consumer information-processing strategies. Journal of Consumer Research, 7(3), 225-233.\nCarenini, G., & Poole, D. (2002). Constructed preferences and value-focused thinking: Implications for ai research on preference elicitation. Paper presented at the AAAI-02 Workshop on Preferences in AI and CP: symbolic approaches.\nCasey, J. (1977). High fructose corn syrup. A case history of innovation. Starch‐Stärke, 29(6), 196-204.\nChen, L., & Pu, P. (2009). Interaction design guidelines on critiquing-based recommender systems. User modeling and user-adapted interaction, 19(3), 167.\nChestnut, R. W., & Jacoby, J. (1977). Consumer information processing: Emerging theory and findings: Graduate School of Business, Columbia University.\nChidamber, S. R., & Kon, H. B. (1994). A research retrospective of innovation inception and success: the technology–push, demand–pull question. International Journal of Technology Management, 9(1), 94-112.\nChoo, C. W., Bergeron, P., Detlor, B., & Heaton, L. (2008). Information culture and information use: An exploratory study of three organizations. Journal of the American Society for Information Science and Technology, 59(5), 792-804.\nClaypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M. (1999). Combing content-based and collaborative filters in an online newspaper.\nComrey, A. L. (1973). A first course in factor analysis: New York, NY: Academic Press.\nCondliff, M. K., Lewis, D. D., Madigan, D., & Posse, C. (1999). Bayesian mixed-effects models for recommender systems. Paper presented at the ACM SIGIR.\nCremonesi, P., Turrin, R., & Airoldi, F. (2011). Hybrid algorithms for recommending new items. Paper presented at the Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems.\nCrum, C., & Palmatier, G. E. (2003). Demand management best practices: process, principles, and collaboration: J. Ross Publishing.\nDavis, F. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319 (1989). In.\nDell`Aglio, D., Celino, I., & Cerizza, D. (2010). Anatomy of a Semantic Web-enabled Knowledge-based Recommender System. Paper presented at the SMRR@ ISWC.\nDi Stefano, G., Gambardella, A., & Verona, G. (2012). Technology push and demand pull perspectives in innovation studies: Current findings and future research directions. Research Policy, 41(8), 1283-1295.\nDierk, S. (1972). The SMART retrieval system: Experiments in automatic document processing—Gerard Salton, Ed.(Englewood Cliffs, NJ: Prentice-Hall, 1971, 556 pp., $15.00). IEEE Transactions on Professional Communication(1), 17-17.\nDrury, D. H., & Farhoomand, A. (1999). Information technology push/pull reactions. Journal of Systems and Software, 47(1), 3-10.\nEdwards, E., & Kasik, D. (1974). User experience with the CYBER graphics terminal. Proceedings of VIM-21, 284-286.\nEkstrand, M. D., Riedl, J. T., & Konstan, J. A. (2011). Collaborative filtering recommender systems. Foundations and Trends® in Human–Computer Interaction, 4(2), 81-173.\nFesenmaier, D. R., Wöber, K. W., & Werthner, H. (2006). Destination recommendation systems: Behavioral foundations and applications: Cabi.\nFornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.\nFritz, W., & Hefner, M. (1981). lntormationsbedarf und Informationsbeschaffung des Konsumenten bei unterschiedlichen Kaufobjekten und Populationen. In Informationsverhalten des Konsumenten (pp. 219-240): Springer.\nGhazanfar, M., & Prugel-Bennett, A. (2010a). Building switching hybrid recommender system using machine learning classifiers and collaborative filtering. IAENG International Journal of Computer Science, 37(3).\nGhazanfar, M., & Prugel-Bennett, A. (2010b). An improved switching hybrid recommender system using naive bayes classifier and collaborative filtering.\nGomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.\nGomez-Uribe, C. A., & Hunt, N. (2016). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 13.\nGorsuch, R. L. (1983). Factor analysis: Hillsdale, N.J. : L. Erlbaum Associates.\nGrabner-Kräuter, S. & Kaluscha, EA 2008. Empirical research in on-line trust: a review and critical assessment. International Journal of Human-Computer Studies, 58.\nHans-Joachim, K. (1981). Informations- und Kaufverhalten unter Zeitdruck. Frankfurt/Main: Peter Lang.\nHartl, J., & Herrmann, R. (2006). The role of business expectations for new product introductions: a panel analysis for the German food industry. Journal of Food Distribution Research, 37(856-2016-57826), 12-22.\nHassenzahl, M. (2003). The Thing and I: Understanding the Relationship Between User and Product. In Funology (pp. 31-42): Springer.\nHassenzahl, M. (2005). The thing and I: understanding the relationship between user and product. In Funology: from usability to enjoyment (pp. 31-42).\nHassenzahl, M. (2008). User experience (UX) towards an experiential perspective on product quality. Paper presented at the Proceedings of the 20th Conference on l`Interaction Homme-Machine.\n\nHee, O. C. (2014). Validity and Reliability of the Customer-Oriented Behaviour Scale in the Health Tourism Hospitals in Malaysia. International Journal of Caring Sciences, 7(3), 771-775.\nHerlocker, J. L., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. Paper presented at the Proceedings of the 2000 ACM conference on Computer supported cooperative work.\nHsu, H.-H., Hsieh, C.-W., & Lu, M.-D. (2011). Hybrid feature selection by combining filters and wrappers. Expert systems with Applications, 38(7), 8144-8150.\nHu, R., & Pu, P. (2011). Enhancing collaborative filtering systems with personality information. Paper presented at the Proceedings of the fifth ACM conference on Recommender systems.\nJacobson, K., Murali, V., Newett, E., Whitman, B., & Yon, R. (2016). Music personalization at Spotify. Paper presented at the Proceedings of the 10th ACM Conference on Recommender Systems.\nHyndman, R.J.& Koehler, A.B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting. Monash University.\nJannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016). Recommender systems—beyond matrix completion. Communications of the ACM, 59(11), 94-102.\nJohn, D. R. (1999). Consumer socialization of children: A retrospective look at twenty-five years of research. Journal of Consumer Research, 26(3), 183-213.\nJones, N., & Pu, P. (2007). User technology adoption issues in recommender systems. Paper presented at the Proceedings of the 2007 Networking and Electronic Commerce Research Conference.\nKaasinen, E., Roto, V., Roloff, K., Väänänen-Vainio-Mattila, K., Vainio, T., Maehr, W., . . . Shrestha, S. (2009). User experience of mobile internet: analysis and recommendations. International Journal of Mobile Human Computer Interaction (IJMHCI), 1(4), 4-23.\nKaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.\nKnijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User modeling and user-adapted interaction, 22(4-5), 441-504.\nKoren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer(8), 30-37.\nKumar, P. V., & Reddy, V. R. (2014). A survey on recommender systems (RSS) and its applications. International Journal of Innovative Research in Computer and Communication Engineering, 2(8), 5254-5260.\nKuss, A. (1987). Information und Kaufentscheidung: Methoden und Ergebnisse\nempirischer Konsumentenforschung. Berlin: Walter de Gruyter.\nLampropoulos, A. S., Lampropoulou, P. S., & Tsihrintzis, G. A. (2012). A cascade-hybrid music recommender system for mobile services based on musical genre classification and personality diagnosis. Multimedia tools and applications, 59(1), 241-258.\nLampropoulos, A. S., Sotiropoulos, D. N., & Tsihrintzis, G. A. (2014). Cascade hybrid recommendation as a combination of one-class classification and collaborative filtering. International Journal on Artificial Intelligence Tools, 23(04), 1460009.\nLee, D. H., Kim, H.-b., & Lee, J. (1991). The impact of research sponsorship upon research effectiveness. Technovation, 11(1), 39-57.\nLekakos, G., & Caravelas, P. (2008). A hybrid approach for movie recommendation. Multimedia tools and applications, 36(1-2), 55-70.\nLight, A. (2001). The influence of context on users` responses to websites. The New Review of Information Behaviour Research, 2(November), 135-149.\nLinden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), 76-80.\nLittlestone, N., & Warmuth, M. K. (1994). The weighted majority algorithm. Information and computation, 108(2), 212-261.\nLü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., & Zhou, T. (2012). Recommender systems. Physics reports, 519(1), 1-49.\nLussier, D. A., & Olshavsky, R. W. (1979). Task complexity and contingent processing in brand choice. Journal of Consumer Research, 6(2), 154-165.\nMcNee, S. M., Riedl, J., & Konstan, J. A. (2006a). Being accurate is not enough: how accuracy metrics have hurt recommender systems. Paper presented at the CHI`06 extended abstracts on Human factors in computing systems.\nMcNee, S. M., Riedl, J., & Konstan, J. A. (2006b). Making recommendations better: an analytic model for human-recommender interaction. Paper presented at the CHI`06 extended abstracts on Human factors in computing systems.\nMooney, R. J., & Roy, L. (2000). Content-based book recommending using learning for text categorization. Paper presented at the Proceedings of the fifth ACM conference on Digital libraries.\nMorone, J. G. (1993). Technology and competitive advantage—The role of general management. Research-Technology Management, 36(2), 16-25.\nNajmani, K., El habib, B., Sael, N., & Zellou, A. (2019). A Comparative Study on Recommender Systems Approaches. Paper presented at the Proceedings of the 4th International Conference on Big Data and Internet of Things.\nNemet, G. F. (2009). Demand-pull, technology-push, and government-led incentives for non-incremental technical change. Research Policy, 38(5), 700-709. doi:10.1016/j.respol.2009.01.004\nNewman, J. W., & Staelin, R. (1972). Prepurchase information seeking for new cars and major household appliances. Journal of Marketing Research, 9(3), 249-257.\nOzok, A. A., Fan, Q., & Norcio, A. F. (2010). Design guidelines for effective recommender system interfaces based on a usability criteria conceptual model: results from a college student population. Behaviour & Information Technology, 29(1), 57-83.\nPazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial intelligence review, 13(5-6), 393-408.\nPennock, D. M., Horvitz, E., Lawrence, S., & Giles, C. L. (2000). Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. Paper presented at the Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence.\nPettigrew, K. E., Fidel, R., & Bruce, H. (2001). Conceptual frameworks in information behavior. Annual review of information science and technology (ARIST), 35(43-78).\nPopp, D. (2002). Induced innovation and energy prices. American economic review, 92(1), 160-180.\nPu, P., & Chen, L. (2006). Trust building with explanation interfaces. Paper presented at the Proceedings of the 11th international conference on Intelligent user interfaces.\nPu, P., & Chen, L. (2010). A User-Centric Evaluation Framework of Recommender Systems.\nPu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. Paper presented at the Proceedings of the fifth ACM conference on Recommender systems.\nPu, P., Chen, L., & Kumar, P. (2008). Evaluating product search and recommender systems for E-commerce environments. Electronic Commerce Research, 8(1-2), 1-27.\nPu, P., Zhou, M., & Castagnos, S. (2009). Critiquing recommenders for public taste products. Paper presented at the Proceedings of the third ACM conference on Recommender systems.\nResnick P,Iacovou N, Suchak M, et al. GroupLens: an open architecture for collaborativefiltering of netnews[C] Proceedings of the 1994 ACM Conference on ComputerSupported Cooperative Work, Oct 22-26, 1994. New York, NY, USA: ACM, 1994:175-186.\nResnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-59.\nRicci, F., Rokach, L., & Shapira, B. (2011). Introduction to Recommender Systems Handbook. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender Systems Handbook (pp. 1-35). Boston, MA: Springer US.\nRocchio, J. J. (1971). The SMART retrieval system: Experiments in automatic document processing. Relevance feedback in information retrieval, 313-323.\nSarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Www, 1, 285-295.\nSarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., & Riedl, J. (1998). Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. Paper presented at the in the GroupLens Research Collaborative Filtering System???. Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW.\nSavolainen, R. (1995). Everyday life information seeking: Approaching information seeking in the context of “way of life”. Library & information science research, 17(3), 259-294.\nSavolainen, R. (1999). Seeking and using information from the Internet: The context of non-work use. In Exploring the contexts of information behaviour (pp. 356-370).\nSchafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data mining and knowledge discovery, 5(1-2), 115-153.\nSchafer, J. B., Konstan, J. A., & Riedl, J. (2002). Meta-recommendation systems: user-controlled integration of diverse recommendations. Paper presented at the Proceedings of the eleventh international conference on Information and knowledge management.\nScholz, M. (2010). Implications of Consumer Information Behaviour to Construct Utility-based Recommender Systems: A Prototypical Study.\nSchön, D. A. (1967). Technology and change: The new Heraclitus (Vol. 8541): Delta.\nSchwab, I., Kobsa, A., & Koychev, I. (2001). Learning user interests through positive examples using content analysis and collaborative filtering. Internal Memo, GMD, St. Augustin, Germany.\nShambour, Q., & Lu, J. (2011). A hybrid trust‐enhanced collaborative filtering recommendation approach for personalized government‐to‐business e‐services. International Journal of Intelligent Systems, 26(9), 814-843.\nShepitsen, A., Gemmell, J., Mobasher, B., & Burke, R. (2008). Personalized recommendation in social tagging systems using hierarchical clustering. Paper presented at the Proceedings of the 2008 ACM conference on Recommender systems.\nShishehchi, S., Banihashem, S. Y., Zin, N. A. M., Noah, S. A. M., & Malaysia, K. (2012). Ontological approach in knowledge based recommender system to develop the quality of e-learning system. Australian Journal of Basic and Applied Sciences, 6(2), 115-123.\nShoham, Y. (1997). Combining content-based and collaborative recommendation. Communications of the ACM.\nSilberer, G. (1981). Das Informationsverhalten des Konsumenten beim Kaufentscheid—Ein analytisch-theoretischer Bezugsrahmen. In Informationsverhalten des Konsumenten (pp. 27-60): Springer.\nSmyth, B., & Cotter, P. (2000). A personalised TV listings service for the digital TV age. Knowledge-Based Systems, 13(2-3), 53-59.\nSong, J., Baker, J., Lee, S., & Wetherbe, J. C. (2012). Examining online consumers’ behavior: A service-oriented view. International Journal of Information Management, 32(3), 221-231.\nSouder, W. E. (1989). Improving productivity through technology push. Research-Technology Management, 32(2), 19-24.\nSteinerová, J., & Šušol, J. (2007). Users` Information Behaviour--A Gender Perspective. Information Research: An International Electronic Journal, 12(3), n3.\nSu, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009.\nSwearingen, K., & Sinha, R. (2002). Interaction design for recommender systems. Paper presented at the Designing Interactive Systems.\nTatiya, R. V., & Vaidya, A. S. (2014). A survey of recommendation algorithms. IOSR Journal of Computer Engineeringf, 16(6), 16-19.\nThorat, P. B., Goudar, R., & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4), 31-36.\nTintarev, N., & Masthoff, J. (2007). A survey of explanations in recommender systems. Paper presented at the 2007 IEEE 23rd international conference on data engineering workshop.\nTran, T., & Cohen, R. (2000). Hybrid recommender systems for electronic commerce. Paper presented at the Proc. Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, Technical Report WS-00-04, AAAI Press.\nVANAMBURG, D. (2019). Even if the Oscar Doesn’t Go to ‘Roma,’ Netflix Has Already Won. Retrieved from https://www.acsimatters.com/2019/02/21/even-if-the-oscar-doesnt-go-to-roma-netflix-has-already-won/\nVenkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.\nWang, Q., Yuan, X., & Sun, M. (2010). Collaborative filtering recommendation algorithm based on hybrid user model. Paper presented at the 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.\nWang, W. (2018). Recommended system of application and development. Paper presented at the AIP Conference Proceedings.\nWilson, T. D. (1981). On user studies and information needs. Journal of documentation, 37(1), 3-15.\nWilson, T. D. (1997). Information behaviour: an interdisciplinary perspective. Information Processing & Management, 33(4), 551-572.\nWilson, T. D. (1999). Models in information behaviour research. Journal of documentation, 55(3), 249-270.\nWilson, T. D. (2000). Human information behavior. Informing science, 3(2), 49-56.\nWinsler, A., Naglieri, J., & Manfra, L. (2006). Children`s search strategies and accompanying verbal and motor strategic behavior: Developmental trends and relations with task performance among children age 5 to 17. Cognitive Development, 21(3), 232-248.\nXiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: use, characteristics, and impact. MIS quarterly, 31(1), 137-209.\nXue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. Paper presented at the Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval.\nZanker, M. (2008). A collaborative constraint-based meta-level recommender. Paper presented at the Proceedings of the 2008 ACM conference on Recommender systems.\nZhang, Q. (2018). The Use of Recommender Systems in Demand Management in Intelligent Supply Chain Management.\nZhao, W. X., Li, S., He, Y., Wang, L., Wen, J.-R., & Li, X. (2016). Exploring demographic information in social media for product recommendation. Knowledge and Information Systems, 49(1), 61-89.\nZiegler, C., McNee, S., & Konstan, J. & Lausen, G. Improving recommendation lists through topic diversification. Paper presented at the Proc. WWW.\nZiegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving recommendation lists through topic diversification. Paper presented at the Proceedings of the 14th international conference on World Wide Web. | 描述: | 碩士 國立政治大學 圖書資訊與檔案學研究所 108155016 |
資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0108155016 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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