1. Julian Keenaghan 1 Personalization of Supermarket Product Recommendations IBM Research Report (2000) R.D. Lawrence et al.

2. Julian Keenaghan2 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

3. Julian Keenaghan3 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)

4. Julian Keenaghan4 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

5. Julian Keenaghan5 Overview Customer Purchase Database Data Mining Associations Data Mining Clustering Product Database Matching Algorithm Cluster-specific Product lists Personalized Recommendation List Normalized customer vectors Cluster assignments Product list for target customer’s cluster Products eligible for recommendation Product affinities

6. Julian Keenaghan6 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

7. Julian Keenaghan7 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..

8. Julian Keenaghan8 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  Lift4.9 Rule: Baby:Disposable Nappies => Baby:Wipes

9. Julian Keenaghan9 Product Model Each product, n, represented by a 2303-dim vector P (n) Individual entries P s (n) reflect the “affinity” the product has to subclass s. P s (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)

10. Julian Keenaghan10 Matching Algorithm Score each product for a specific customer and select the best matches. Cosine coefficient metric used C is the customer vector P is the product vector σ mn is the score between customer m and product n σ mn = ρ n C (m). P (n) / ||C (m) || ||P (n) ||

11. Julian Keenaghan11 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

12. Julian Keenaghan12 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

13. Julian Keenaghan13 Summary Product recommendation system for grocery shopping Content and Collaborative filtering  Purchasing history  Associations Mining  Clustering Revenue boosts ~2%