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研究生:王浩丞
研究生(外文):WANG, HAO-CHENG
論文名稱:利用關聯規則建構企業間商品推薦系統
論文名稱(外文):Constructing Recommender System based on Association Rules for B2B company
指導教授:許中川許中川引用關係
指導教授(外文):HSU, CHUNG-CHIAN
口試委員:蔡鴻旭賴怡洵
口試委員(外文):TSAI, HUNG-HSULAI, YI-HSUN
口試日期:2017-06-07
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:36
中文關鍵詞:推薦系統關聯規則歷史交易資料分析Apriori演算法B2B
外文關鍵詞:Recommender SystemAssociation RulesAnalysis of transaction dataApriori AlgorithmBusiness-to-Business
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近年來電子商務的興起,使得大多數B2C企業開始重視網路銷售渠道,並導入線上購物平台。而該平台通常與產品推薦系統做結合,以提高顧客購買更多產品的可能性。然而,B2B企業通常很少建立網路購物平台,且沒有進一步的分析銷售交易數據,更不用說建立推薦系統,即使它們已經收集了大量的歷史交易資料。若沒有推薦系統,公司可能會錯過許多潛在的銷售機會。本研究提出了一個B2B推薦系統的雛形,透過關聯規則演算法應用於大型B2B公司之歷史交易數據進行分析,以吸引客戶購買更多產品並提高公司利潤。在本研究中,我們識別出了存在於歷史交易中的四種類型的異常值,這些異常值將會被移除。此外,本研究透過移動窗口方法合併單項類型之交易資料。實驗結果顯示,採用移動窗口合併單項交易後,有助於提高關聯規則之績效。
In recent years, the rise of e-commerce makes the majority of B2C enterprises begin to pay attention to the network sales channel and introduce the online shopping platform. The platforms usually include a product recommender system in order to improve the probability of customer buying more products. However, B2B companies seldom build online shopping platform and didn't further analyze sales transactions, not to mention a recommendation system, although they have already collected considerable amount of historical transactions. If there is no recommender system, the company may lose a lot of potential sales. This study proposed a prototype of recommender system which uses the association-rules algorithm to analyze historical transaction data of a large B2B company, in order to attract customers to buy more products and increase the company profits. In the study, we identify four types of outliers in historical transactions which shall be removed. We also use a merging process with moving window to deal with single-item transactions. The experimental result shows the merge with the moving window process improves rules performance.
摘要.................................................................................................................................i
ABSTRACT.............................................................................................................................ii
誌謝.................................................................................................................................iii
Table of content.....................................................................................................................iv
List of Tables.......................................................................................................................v
List of Figures......................................................................................................................vi
1. Introduction......................................................................................................................1
1.1 Background.......................................................................................................................1
1.2 Motivation.......................................................................................................................1
1.3 Objective........................................................................................................................2
2. Literature review.................................................................................................................3
2.1 Recommender System...............................................................................................................3
2.1.1 Content-Based Methods..........................................................................................................4
2.1.2 Collaborative Methods..........................................................................................................5
2.1.3 Hybrid Methods.................................................................................................................6
2.2 Association Rules................................................................................................................6
2.2.1 Apriori algorithm..............................................................................................................7
3. Methodology.......................................................................................................................8
3.1 Data preprocessing...............................................................................................................10
3.2 Association rule mining..........................................................................................................13
3.3 Recommender system...............................................................................................................15
4. Experiments.......................................................................................................................17
4.1 Dataset..........................................................................................................................17
4.2 Preprocessing....................................................................................................................17
4.3 Recommendation phase.............................................................................................................21
4.3.1 Search recommendation products...............................................................................................22
4.3.2 Compare the category between the candidate product and the target product.............................22
4.4 Compare the result between merge virtual trans. and without merge...................................22
5. Conclusions.......................................................................................................................25
References...........................................................................................................................26

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