以消費者意圖為基礎的商品推薦 以生鮮超市為例

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姓名

張安媞(An-Ti Chang) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

以消費者意圖為基礎的商品推薦以生鮮超市為例
(Product Recommendation for Supermarket Industry – A Consumer Intension-Based Approach)

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摘要(中)

對零售商而言，每天都有大量的交易資料產生，如何善加利用手上資料來更了解消費者並進行行銷活動是件重要的事情，其中包括進行消費者區分、了解消費者行為和進行商品推薦，在過往常會利用RFM(recency, frequency and monetary) 或是PPS(Purchased products structure) 分析來進行消費者區分，然而本研究則透過TF-IDF及LDA分析方法來尋找消費者意圖，以此進行消費者區分，並運用區分好的消費者意圖，以MST(minimum spanning tree)進行購物籃分析來了解消費者行為和找出關鍵產品，最後運用Lift值計算出關鍵產品排序，藉此找出不同消費者意圖下的消費行為並進行商品推薦。

摘要(英)

For retailers, a large amount of transaction data is generated every day, how to make good use of the data at hand to better understand consumers and carry out marketing activities is important, including consumer differentiation, understanding consumer behavior and conducting product recommendation. For customer segmentation in the past, Recency, Frequency and Monetary (RFM) or Purchased products structure (PPS) analysis was usually used. However, this study uses Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) analysis methods to find consumer intentions to use on customer segmentation. Base on the segment of consumer intentions to conduct shopping basket analysis with Minimum Spanning Tree (MST) to understand consumer behavior and find the key products. Finally, use Lift score to calculate the key product ranking, so as to find out the consumption behaviors under different consumer intentions and make product recommendations.

關鍵字(中)

★ 消費者意圖
★ 自然語言處理TF-IDF
★ 文本分析LDA
★ 最小生成樹
★ 商品推薦
★ 關聯法則
★ 超商行銷

關鍵字(英)

★ Shopping Mission
★ Term Frequency–Inverse Document Frequency
★ Latent Dirichlet Allocation
★ Minimum Spanning Tree
★ Product Recommendations
★ Association Rule
★ Supermarket Marketing

論文目次

Chinese Abstract ......................................................................................................................i
English Abstract ......................................................................................................................ii
Contents ..................................................................................................................................iii
List of Figure…………………………………………………………………………….…...v
List of Table……………………………………………………………………………….... vi
Chapter1 Introduction..............................................................................................................1
Chapter2 Literature review .....................................................................................................3
2.1 Shopping Mission………………………………………………………………...... 3
2.2 Topic……………………………………………………………………………...... 3
2.2.1 TF-IDF………………………………………………………………….…... 4
2.2.2 LDA…………………………………………………………………….…... 4
2.3 MST………………………………………………………………………………... 4
2.4 Association Rule Mining…………………………………………………………...5
2.4.1 Apriori…………………………………………………………………….... 5
Chapter3 Methodology……………………………………………………………………... 6
3.1 Data Pre-processing……………………………………………………………...... 6
3.2 Topic Generated………………………………………………………………….... 7
3.2.1 TF-IDF…………………………………………………………………....... 7
3.2.2 LDA………………………………………………………………………... 8
3.3 Prim’s MST Key Product Identification…………………………………………...9
3.3.1 Binary Matrix Construction…………………………………………… ….11
3.3.2 Correlation Matrix Construction…………………………………………..12
3.3.3 Distance Matrix Construction……………………………………………..12
3.3.4 MST Construction………………………………………………………....13
3.3.5 Mutual Information and Permutation………………………………… ….14
3.3.6 Key product Identification………………………………………………. .16
3.4 Product Recommendation………………………………………………………. .17
Chapter4 Conclusion……………………………………………………………………... .18
Chapter5 Reference……………………………………………………………………….. 19

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指導教授

陳炫碩(Xuan-Shuo Chen)

審核日期

2022-9-30

推文