Download Data Mining - Network Computing Lab

『Personalization of Supermarket
Product Recommendations』
20015065
김용수
1.
2.
3.
4.
5.
Introduction
Overview of the System
Data Mining Analysis
Application
Reference
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)
2. Overview of the System (1)
Shopping orders
PDA
SmartPad Server
-device proxy
- remote ordering server
(ROS)
Updated PDBs
Dial –in
networ
k
PDA
- SmartPad
SmartPad Database
Transactional Data
Picker orders
Operational
Mainframe
PDA
Browser
Legacy Database
Web Server
Farm
Browser
POS
POS
POS
POS
Browser
SmartPad System
Existing operational system
- Product Information
- Customer spending
histories
2. Overview of the System (2)
Normalized
Customer
vectors
Data Mining
Clustering
Product
Database
Customer
Purchase
Database
Cluster
assignments
Products eligible
for
recommendation
- Recommender System
Grouping between customer & product
Cluster-specific
Product lists
Products List
For target customer’s
cluster
Vector for
Target customer
Matching
Algorithm
Personalized
Recommendation
List
Product
affinities
Data Mining
Associations
Target Customer
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.
2.
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.
3. Data Mining Analysis (3)
▶ Association Rule- Measure
- Support (A
B) =
number of transactions containing both A and B
Total number of transactions
=
- Confidence (A
P(A ∩ B)
B) =
number of transactions
containing both A and B

number of transactions containing A
=
P(A ∩ B)
P(A)
= P(B | A)
3. Data Mining Analysis (4)
▶ Association Rule- Example
Purchased products
A
B
C
D
E
F
Customer 1
1
0
0
0
0
1
Customer 2
1
1
0
1
0
1
Customer 3
1
0
1
1
0
1
Customer 4
1
0
0
1
0
1
Customer 5
1
1
0
0
1
0
Step1: Find all frequent itemsets.
Support of A & D = 3/5 = 0.6
Support of A & F = 4/5 = 0.8
Support of A & E = 1/5= 0.2
Minimum support = 60%
Large Itemset
# of transactions
Support (%)
A
5
100
D
3
60
F
4
80
A,D
3
60
A,F
4
80
D,F
3
60
A,D,F
3
60
3. Data Mining Analysis (5)
Step2: Generate strong association rules from the frequent itemsets.
Rules
Support P(A ∩ B)
Prob. Of Conditions
Confidence
A F
80 %
100 %
0.8
A D
60%
100 %
0.6
DF
60 %
60 %
1
D, F  A
60 %
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 : 200
- Recommendation Products per each customer : 10~20
4. Application (2)
- Safeway Stores
▶ Safeway product taxonomy
Product classes (99)
Soft Drinks
Product subclasses (2302)
Products (~30000)
Petfoods
Dried
Dried
Canned Canned
Cat Food Dog Food Cat FoodDog Food
Friskies
Liver (250g)
Problem : Multilevel Products (Data Mining Issue)
Seasonal Products
Tea
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.)
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, 2001.
▶ Agrawal, R. and Srikant, R., Fast Algorithms for mining association rules,
In proc. of the VLDB Conf., 1994