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題名:以顧客價值分析與權重漸進探勘來進行協力式音樂推薦
書刊名:資訊、科技與社會學報
作者:林朝興唐瑩荃
作者(外文):Lin, Chow-singTang, Ying-quan
出版日期:2006
卷期:6:1=10
頁次:頁1-26
主題關鍵詞:權重漸進探勘音樂推薦系統RFM模組協力推薦關聯式規則Incremental mining based on weightMusic recommendation systemRFM modelCollaborative filteringAssociation rule mining
原始連結:連回原系統網址new window
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  • 被引用次數被引用次數:期刊(5) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:5
  • 共同引用共同引用:0
  • 點閱點閱:71
現今網際網路快速的發展,大量的數位音樂,已經廣泛在網路上傳播,造成使用者無法隨心所欲的找到想要的音樂歌曲。許多電子商務更是發展音樂推薦系統來提高顧客需求慾望。而一般線上音樂推薦系統,記錄了使用者所有歷史交易資料,並全部進行分析。因此,便增加執行時所耗費的成本、時間及是否符合使用者目前真正喜好的項目。本論文利用RFM模組來分析顧客價值,並且將相同顧客價值歸為同一群組,進而達到分群的動作。結合使用者最近習慣,提出以權重漸進探勘(Incremental Mining based on Weight)的構想,以漸進增加交易資料量的方式來探勘最近規則,而不需將全部交易資料都做分析,藉以節省計算成本、時間,並以Apriori演算法來探勘關聯式規則。而用相似向量矩陣計算使用者們之間的相似度關係,便利相似聚集。最後利用協力式推薦(Collaborative Filtering)的概念,由推薦模組將音樂推薦給使用者,做為個人化推薦方式。 實驗結果顯示,結合RFM模組及相似聚集推薦較單純只使用RFM分群方式為佳。此外,利用權重漸進與分群方式,更能夠推薦使用者喜好的音樂。而整體上,本論文的推薦準確率高達0.78,比其他推薦方法高出15%~32%,有效的達到個人化推薦的效果。
Because of the rapid development of internet network, the large amount of digital music has spread extensively on the Internet. That causes users cannot follow one’s bent to find out the music or songs they want. Many e-commerce make further efforts to develop Music Recommendation System to improve customers’ demands and desires. The general on-line Music Recommendation System records all user’s former transaction and analysis them completely. So, it increases the cost, time, and items which adapt to what users like now or not. This paper combines RFM model to analysis customers’ value and classify the same one as the same group. We combine users’ Recent Behavior to Incremental Mining based on Weight, which can mine for relations by it, not analyzing all data, to decrease to calculate cost and time. It also prospect for Association Rule Mining by the Apriori algorithms. And then, similar vector matrix is used to calculate the degree of similarity relation between users’ to assemble them conveniently. Finally, through the concept of Collaborative Filtering, we take advantage of recommendation model to be the method of individual recommendation that put music up to users. According to the experiment results, it is better to use the combination of RFM model and similar assembling than RFM classification only. What is more, it also shows that using IMW and classification model can recommend fitfully music what users like. In the whole, the accuracy of this research is up to 0.78, which is higher 15% to 32% than the others, to make up the effect of individual recommendation.
期刊論文
1.Liu, D. R.、Shih, Y. Y.(2005)。Integrating AHP and Data Mining for Product Recommendation Based on Customer Lifetime Value。Information & Management,42,387-400。  new window
2.Schafer, J. B.、Konstan, J. A.、Riedl, J.(2001)。E-Commerce Recommendation Applications。Data Mining and Knowledge Discovery,5(1/2),115-153。  new window
3.Goldberg, D.、Nichols, D.、Oki, B. M.、Terry, D.(1992)。Using collaborative filtering to weave an information tapestry。Communications of the ACM,35(12),61-70。  new window
4.Balabanovic, M.、Shoham, Y.(1997)。Fab: Content-based Collaborative Filtering Recommendation。Communications of the ACM,40,66-72。  new window
5.Cho, Y. H.、Kim, J. K.(2004)。Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce。Expert Systems with Applications,26,233-246。  new window
6.Cho, Y. B.、Cho, Y. H.、Kim, S. H.(2005)。Mining changes in customer buying behavior for collaborative recommendations。Expert Systems with Applications,28(2),359-369。  new window
7.Kim, J. K.、Cho, Y. H.、Kim, W. J.、Kim, J. R.、Suh, J. H.(2004)。A personalized recommendation procedure for Internet shopping support。Electronic Commerce Research and Applications,1,301-313。  new window
8.Saracevic, T.、Kantor, P.、Chamis, A. Y.、Trivision, D.(1998)。A Study in Information Seeking and Retrieving. II User, Questions and Effectiveness。Journal of the American Society for Information Science,39,176-195。  new window
9.Weng, S. S.、Liu, M. -J.(2004)。Feature-based recommendations for one-to-one marketing。Expert Systems with Applications,26,493-508。  new window
會議論文
1.Lang, K.(1995)。News Weeder: Learning to Filter Netnews。The 12th International Conference on Machine Learning,331-339。  new window
2.Schafer, J. B.、Konstan, J. A.、Riedi, J.(1999)。Recommender systems in e-commerce。The 1st ACM Conference on Electronic Commerce。Denver, CO:ACM。158-166。  new window
3.Agrawal, R.、Imielinski, T.、Swami, A. N.(1993)。Mining Association Rules between Sets of Items in Large Databases。The 1993 ACM SIGMOD International Conference on Management of Data,207-216。  new window
4.Shardanand, U.、Maes, P.(1995)。Social information filtering: Algorithms for automating "word of mouth"。The SIGCHI Conference on Human Factors in Computing Systems。Denver, Colorado:ACM Press/Addison-Wesley Publishing Co。210-217。  new window
5.Agrawal, R.、Srikant, R.(1994)。Fast algorithms for mining association rules in large database。The 20th International Conference on Very Large Data Bases。Morgan Kaufmann Publishers Inc.。478-499。  new window
6.Basu, C.、Haym, H.、Cohen,W. -W.(1998)。Recommendation as Classification: Using Social and Content-Based Information in Recommendation。  new window
7.Chen, H. -C.、Chen, Arbee L. P.(2001)。Collaborative Filtering and Algorithms: A Music Recommendation System Based on Music Data Grouping and User Interests。  new window
8.Kaymak(2001)。Fuzzy target selection using RFM variables2,1038-1043。  new window
9.Kuo, F. -F.、Shan, M. -K.(2002)。A Personalized Music Filtering System Based on Melody Style Classification。Japan。9-12。  new window
10.Shan, M. -K.、Kuo, F. -F.、Chen,M. -F.(2002)。Music Style Mining and Classification by Melody1,26-29。  new window
11.Wu, Y. -H.、Chen, Arbee L. P.(2001)。Enabling Personalized Recommendation on the Web Based on User Interests and Behaviors。  new window
學位論文
1.汪軒楷、王台平(2002)。策略式資料探勘在個人化推薦上之研究。私立真理大學。  延伸查詢new window
圖書
1.Hughes, Arthur M.(1994)。Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program。Probus Publishing Company。  new window
2.Han, Jiawei、Kamber, Micheline(2000)。Data mining: Concepts and techniques。Morgan Kaufmann Publishers。  new window
 
 
 
 
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