Julian Keenaghan 1

Personalization of Supermarket Product Recommendations

IBM Research Report (2000)R.D. Lawrence et al.

Julian Keenaghan 2

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

Julian Keenaghan 3

Introduction continued 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)

Julian Keenaghan 4

Product Taxonomy

Classes (99)

Subclasses (2302)

Products (~30000)

Fresh Beef

Petfoods …..Soft Drinks …..

Dried Cat Food

Dried Dog Food

Canned Cat Food

Friskies Liver (250g)

Beef Joints

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Overview

Customer Purchase Database

Data MiningAssociations

Data MiningClustering Product

Database

MatchingAlgorithm

Cluster-specificProduct lists

Personalized Recommendation

List

Normalized

customer

vectors

Cluster

assignments

Product list

for target customer’s

cluster

Products eligible

for recommendation

Product affinities

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Customer Model

Customer profileVector, C(m)

s, for each customer

At subclass level => 2303 dim spaceNormalized fractional spending

quantifies customer’s 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 Clusters evaluated in terms of dominant attributes:

products which most distinguish members of the cluster Cluster 1 – Wines/Beers/Spirits Cluster 2 – Frozen foods Cluster 3 - Baby products, household items etc..

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Associations Mining Determine relationships among product classes or subclasses Used IBM’s “Intelligent Miner for Data”

Apriori 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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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) =

0 otherwise

0.25 if C(n) C(s) (associated class)

0.5 if C(s) = C(n) (same class)

1.0 if S(n) s (associated subclass)

1.0 if s = S(n) (same subclass)

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Matching Algorithm

Score each product for a specific customer and select the best matches.

Cosine coefficient metric usedC 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)||

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Matching Algorithm ctd.

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

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Results

Recommendations generated weekly 8 months, 200 customers from one 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. Wine vs. household care. “interesting” recommendations

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Summary

Product recommendation system for grocery shopping

Content and Collaborative filteringPurchasing historyAssociations MiningClustering

Revenue boosts ~2%