business intelligence & data mining-17

8/10/2019 Business Intelligence & Data Mining-17

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Personalization of

Supermarket Product

Recommendations

R.D. Lawrence et al.


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Introduction

Personalized recommender systemdesigned to suggest newproducts to

supermarket shoppers

Based upon their previous purchasebehaviour and expected product appeal

Shoppers use PDAs

Alternative source of new ideas


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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)


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Product Taxonomy

Classes(99)

Subclasses(2302)

Products(~30000)

FreshBeef

Petfoods ..Soft Drinks..

DriedCatFood

DriedDogFood

CannedCatFood

FriskiesLiver(250g)

BeefJoints


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5

Overview

CustomerPurchaseDatabase

Data MiningAssociations

Data MiningClustering Product

Database

MatchingAlgorithm

Cluster-specificProduct lists

PersonalizedRecommendation

List

Normalized

customer

vectors

Cluster

assignments

Product list

for target customers

cluster

Products eligiblefor recommendation

Productaffinities


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Customer Model

Customer profile

Vector, C(m)s, for each customer

At subclass level => 2303 dim space

Normalized fractional spending quantifies customers interest in subclass relative

to entire customer database

value of 1 implies average level of interest in a

subclass


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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 ofSOM)

Clusters evaluated in terms of dominant attributes:products which most distinguish members of the cluster

Cluster 2Frozen foodsCluster 3Wines/Beers/SpiritsCluster 4 - Baby products, household items etc..


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Significant Product Classes


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Within Cluster Product Popularity


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Associations Mining

Determine relationships among productclasses or subclasses

Used IBMs Intelligent MinerApriori algorithm

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

Support 0.016

Confidence 0.33

Lift 4.9 Rule:Baby:Disposable Nappies => Baby:Wipes


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Matching Algorithm

Score each product for a specific customerand select the best matches.

Cosine similarity metric used

C is the customer vector

P is the product vector

mnis the score between customer m and product n

mn= n C(m).P(n)/||C(m)|| ||P(n)||


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Matching Algorithm

Limit recommendations for each customerto 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


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Results

Recommendations generated weekly 8 months, 200 customers from each store

Respectable 1.8% boost in revenue frompurchases from the list of recommendedproducts.

Accepted Recommendations from productclasses new to the customer

Certain products more amenable torecommendations.

Interesting recommendations: Wine vs.household care.


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