『 Personalization of Supermarket Product Recommendations 』

1. 『Personalization of Supermarket Product Recommendations』 Start 20015065 김용수

2. Contents 1. Introduction 2. Overview of the System 3. Data Mining Analysis 4. Application 5. Reference

3. 1. Introduction ▶Research Objective • Design of the personalized recommender system (SmartPad System) ▶Project group - Safeway Stores in UK ( Data offering & Application) - IBM ( System design & Data analysis) ▶Concept • Suggestion of new products to supermarket shoppers based on their previous • purchase behavior • - Using PDA (Personal Digital Assistant)

4. Product Information • Customerspending • histories 2. Overview of the System (1) - SmartPad • SmartPad Server • device proxy • remote ordering server • (ROS) Shopping orders SmartPad Database Updated PDBs PDA Dial –in network Transactional Data PDA Picker orders Legacy Database Operational Mainframe PDA Web Server Farm Browser Browser POS POS POS POS Browser SmartPad System Existing operational system

5. Grouping between customer & product Grouping betweenproducts 2. Overview of the System (2) - Recommender System Normalized Customer vectors Product Database Customer Purchase Database Data Mining Clustering Cluster assignments Products eligible for recommendation Cluster-specific Product lists Products List For target customer’s cluster Vector for Target customer Matching Algorithm Data Mining Associations Product affinities Personalized Recommendation List Target Customer

6. Matching Algorithm (Key points in this paper) 3. Data Mining Analysis (1) ▶Clustering • Neural Clustering Algorithm • Demographic Clustering Algorithm ▶Association Rule • Apriori Algorithm • AprioriAll Algorithm • AprioriTid Algorithm • DynamicSome Algorithm • FP-Growth

7. 3. Data Mining Analysis (2) ▶Association Rule- Concept • Search for interesting relationships among items in a given data set. ▶Association Rule- Procedure • Find all frequent itemsets. ; Each of these itemsets will occur at least as frequently as a pre-determined minimum support. • Generate strong association rules from the frequent itemsets.; These rules must satisfy minimum support and minimum confidence.

8. 3. Data Mining Analysis (3) ▶Association Rule- Measure number of transactions containing both A and B • Support (A B) = Total number of transactions = P(A B) ∩ number of transactions containing both A and B • Confidence (A B) = number of transactions containing A P(A B) ∩ = P(B | A) = P(A)

9. 3. Data Mining Analysis (4) ▶Association Rule- Example Support of A & D = 3/5 = 0.6 Support of A & F = 4/5 = 0.8 Support of A & E = 1/5= 0.2 Step1: Find all frequent itemsets. Minimum support = 60%

10. 3. Data Mining Analysis (5) Step2: Generate strong association rules from the frequent itemsets. AD : Confidence = 60%/100%= 0.6, D  F : Confidence = 60%/60% = 1 Minimum Confidence = 90% Strong Association Rule : D F , etc

11. 4. Application (1) - Safeway Stores ▶Data Collection • Duration : 7 months • Number of Customers : 200 • Recommendation Products per each customer : 10~20

12. Problem : Multilevel Products (Data Mining Issue) Seasonal Products 4. Application (2) - Safeway Stores ▶Safeway product taxonomy Product classes (99) Petfoods Tea Soft Drinks Dried Cat Food Dried Dog Food Canned Cat Food Canned Dog Food Product subclasses (2302) Products (~30000) Friskies Liver (250g)

13. This system can be used a reasonable tool for recommending new products in Supermarket. 4. Application (3) - Safeway Stores ▶Results • 1957 products were recommended. Of these, 120(6.1%) were chosen. • (It is important to recall that the recommendation list will contain no products • previously purchased by this customer.)

14. 5. References Lawrence, R. D., Almasi, G.S., Kotlyar, V., Viveros, M.S., and Duri, S.S., “Personalization of Supermarket Product Recommendations”, Data mining and Knowledge Discovery, Vol.5, No.1, 11-32, 2001. ▶ Agrawal, R. and Srikant, R., Fast Algorithms for mining association rules, In proc. of the VLDB Conf., 1994 ▶

15. 끝 감사합니다