『 Personalization of Supermarket Product Recommendations 』 김용수

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

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

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

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

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

3. Data Mining Analysis (2) ▶ Association Rule- Concept - Search for interesting relationships among items in a given data set. ▶ Association Rule- Procedure 1.Find all frequent itemsets. ; Each of these itemsets will occur at least as frequently as a pre-determined minimum support. 2.Generate strong association rules from the frequent itemsets. ; These rules must satisfy minimum support and minimum confidence.

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

3. Data Mining Analysis (4) ▶ Association Rule- Example Purchased products ABCDEF Customer Customer Customer Customer Customer Support of A & D = 3/5 = 0.6 Support of A & F = 4/5 = 0.8 Support of A & E = 1/5= 0.2 Large Itemset# of transactionsSupport (%) A5100 D360 F480 A,D360 A,F480 D,F360 A,D,F360 Minimum support = 60% Step1: Find all frequent itemsets.

3. Data Mining Analysis (5) Step2: Generate strong association rules from the frequent itemsets. Rules Support P(A ∩ B) Prob. Of ConditionsConfidence A  F 80 %100 %0.8 A  D 60%100 %0.6 D  F 60 % 1 D, F  A 60 % 1 A  D : Confidence = 60%/100%= 0.6, D  F : Confidence = 60%/60% = 1 Minimum Confidence = 90% Strong Association Rule : D F, etc

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

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

4. Application (3) - Safeway Stores ▶ Results 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.) This system can be used a reasonable tool for recommending new products in Supermarket.

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, ▶ Agrawal, R. and Srikant, R., Fast Algorithms for mining association rules, In proc. of the VLDB Conf., 1994