Business Intelligence & Data Mining-17

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Business Intelligence & Data Mining-17

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Content

Personalization of
Supermarket Product
Recommendations
R.D. Lawrence et al.

Introduction
• Personalized recommender system
designed to suggest new products to
supermarket shoppers
• Based upon their previous purchase
behaviour and expected product appeal
• Shoppers use PDA’s
• Alternative source of new ideas

Usual Techniques for Product
Recommendations
• Content-based filtering
– based on what person has liked in the past
– measure of distance between vectors representing:
• Personal preferences
• Products

– overspecialization


Collaborative filtering




items that similar people have liked
Associations mining (product domain)
Clustering (customer domain)

Product Taxonomy
Classes
(99)

Subclasses
(2302)

Products
(~30000)

Soft Drinks
…..

Dried
Cat
Food

Petfoods

Dried
Dog
Food

…..

Fresh
Beef

Beef
Joints

Canned
Cat
Food

Friskies
Liver
(250g)

Overview
Normalized
customer

Customer
Purchase
Database

vectors

Data Mining
Clustering

Product
Database

Cluster
assignments
Products eligible
for recommendation
Cluster-specific
Product lists

Product list
Data Mining
Associations

for target customer’s
cluster
Product
affinities

Matching
Algorithm

5

Personalized
Recommendation
List

Customer Model
• Customer profile
– Vector, C(m)s, for each customer
– At subclass level => 2303 dim space
– Normalized fractional spending
• quantifies customer’s interest in subclass relative
to entire customer database
• value of 1 implies average level of interest in a
subclass

Clustering Analysis
• To identify groups of shoppers with similar spending
histories
• Cluster-specific list of popular products used as input to
recommender
• Clustered at 99-dim product-class level
• Neural, demographic clustering algorithms (a type of
SOM)
• Clusters evaluated in terms of dominant attributes:
products which most distinguish members of the cluster




Cluster 2 – Frozen foods
Cluster 3 – Wines/Beers/Spirits
Cluster 4 - Baby products, household items etc..

Product Level Clusters

Significant Product Classes

Within Cluster Product Popularity

Associations Mining
• Determine relationships among product
classes or subclasses
• Used IBM’s “Intelligent Miner”
– Apriori algorithm

• Support, Confidence, Lift factors
• Rule: Fresh Beef => Pork/Lamb
– Support
– Confidence
– Lift

0.016
0.33
4.9

• Rule: Baby:Disposable Nappies => Baby:Wipes

Sample Association Rules

Product Model
• Each product, n, represented by a 2303-dim vector P(n)
• Individual entries Ps(n) reflect the “affinity” the product has
to subclass s.

Ps(n) =

1.0

if s = S(n)

(same subclass)

1.0

if S(n)  s

(associated subclass)

0.5

if C(s) = C(n)

(same class)

0.25 if C(n)  C(s)
0

otherwise

(associated class)

Matching Algorithm
• Score each product for a specific customer
and select the best matches.
• Cosine similarity metric used
C is the customer vector
P is the product vector
σ

mn is

the score between customer m and product n

σmn = ρn C(m). P(n) / ||C(m)|| ||P(n)||

Matching Algorithm
• Limit recommendations for each customer
to 1 per product subclass, and 2 per class
• 10 to 20 products returned to PDA
• Previously bought products excluded
• Data from 20,000 customers
• Recommendations for 200

Results
• Recommendations generated weekly
• 8 months, 200 customers from each store
• “Respectable” 1.8% boost in revenue from
purchases from the list of recommended
products.
• Accepted Recommendations from product
classes new to the customer
• Certain products more amenable to
recommendations.
• Interesting recommendations: Wine vs.
household care.

Summary
• Product recommendation system for
grocery shopping
• Content and Collaborative filtering
– Based on purchasing history
– Associations Mining
– Clustering

• Revenue boosts ~2%

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