Presentation is loading. Please wait.

Presentation is loading. Please wait.

『 Personalization of Supermarket Product Recommendations 』 20015065 김용수.

Published byGeorge Parrish Modified over 9 years ago

Similar presentations

Presentation on theme: "『 Personalization of Supermarket Product Recommendations 』 20015065 김용수."— Presentation transcript:

1 『 Personalization of Supermarket Product Recommendations 』 20015065 김용수

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

3 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)

4 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

5 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

6 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)

7 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.

8 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)= ∩

9 3. Data Mining Analysis (4) ▶ Association Rule- Example Purchased products ABCDEF Customer 1100001 Customer 2110101 Customer 3101101 Customer 4100101 Customer 5110010 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.

10 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

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

12 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

13 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.

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

Download ppt "『 Personalization of Supermarket Product Recommendations 』 20015065 김용수."

Similar presentations

Frequent Itemset Mining Methods. The Apriori algorithm Finding frequent itemsets using candidate generation Seminal algorithm proposed by R. Agrawal and.

Frequent Itemset Mining Methods. The Apriori algorithm Finding frequent itemsets using candidate generation Seminal algorithm proposed by R. Agrawal and.
A distributed method for mining association rules

A distributed method for mining association rules
Data Mining Techniques Association Rule

Data Mining Techniques Association Rule
Association Rules Spring 2010. Data Mining: What is it?  Two definitions:  The first one, classic and well-known, says that data mining is the nontrivial.

Association Rules Spring Data Mining: What is it?  Two definitions:  The first one, classic and well-known, says that data mining is the nontrivial.
LOGO Association Rule Lecturer: Dr. Bo Yuan

LOGO Association Rule Lecturer: Dr. Bo Yuan
IT 433 Data Warehousing and Data Mining Association Rules Assist.Prof.Songül Albayrak Yıldız Technical University Computer Engineering Department

IT 433 Data Warehousing and Data Mining Association Rules Assist.Prof.Songül Albayrak Yıldız Technical University Computer Engineering Department
10 -1 Lecture 10 Association Rules Mining Topics –Basics –Mining Frequent Patterns –Mining Frequent Sequential Patterns –Applications.

10 -1 Lecture 10 Association Rules Mining Topics –Basics –Mining Frequent Patterns –Mining Frequent Sequential Patterns –Applications.
Chase Repp.  knowledge discovery  searching, analyzing, and sifting through large data sets to find new patterns, trends, and relationships contained.

Chase Repp.  knowledge discovery  searching, analyzing, and sifting through large data sets to find new patterns, trends, and relationships contained.
Sampling Large Databases for Association Rules ( Toivenon’s Approach, 1996) Farzaneh Mirzazadeh Fall 2007.

Sampling Large Databases for Association Rules ( Toivenon’s Approach, 1996) Farzaneh Mirzazadeh Fall 2007.
Privacy Preserving Association Rule Mining in Vertically Partitioned Data Reporter ： Ximeng Liu Supervisor: Rongxing Lu School of EEE, NTU

Privacy Preserving Association Rule Mining in Vertically Partitioned Data Reporter ： Ximeng Liu Supervisor: Rongxing Lu School of EEE, NTU
Data Mining Techniques Cluster Analysis Induction Neural Networks OLAP Data Visualization.

Data Mining Techniques Cluster Analysis Induction Neural Networks OLAP Data Visualization.
Rakesh Agrawal Ramakrishnan Srikant

Rakesh Agrawal Ramakrishnan Srikant
Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach,

Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach,
Data Mining Association Rules Yao Meng Hongli Li 91.574 Database II Fall 2002.

Data Mining Association Rules Yao Meng Hongli Li Database II Fall 2002.
4/3/01CS632 - Data Mining1 Data Mining Presented By: Kevin Seng.

4/3/01CS632 - Data Mining1 Data Mining Presented By: Kevin Seng.
CS 590M Fall 2001: Security Issues in Data Mining Lecture 5: Association Rules, Sequential Associations.

CS 590M Fall 2001: Security Issues in Data Mining Lecture 5: Association Rules, Sequential Associations.
Association Rules Presented by: Anilkumar Panicker Presented by: Anilkumar Panicker.

Association Rules Presented by: Anilkumar Panicker Presented by: Anilkumar Panicker.
DATA MINING -ASSOCIATION RULES-

DATA MINING -ASSOCIATION RULES-
1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Association Rule Mining Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.

1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Association Rule Mining Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential.
Summarization of Frequent Pattern Mining. What is FPM? Why being frequent is so important? Application of FPM Decision make/Business Software Debugging.

Summarization of Frequent Pattern Mining. What is FPM? Why being frequent is so important? Application of FPM Decision make/Business Software Debugging.

Similar presentations

Ads by Google