Personalization of Supermarket Product Recommendations

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) Fresh Beef Soft Drinks ….. Petfoods ….. Subclasses (2302) Dried Cat Food Beef Joints Dried Dog Food Canned Cat Food Friskies Liver (250g) Products (~30000) Julian Keenaghan

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

6. 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 Julian Keenaghan

7. 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.. Julian Keenaghan

8. 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 Julian Keenaghan

9. 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) = Product Model 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) (associated class) 0 otherwise Julian Keenaghan

10. 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 σmnis the score between customer m and product n σmn = ρn C(m).P(n)/ ||C(m)|| ||P(n)|| Julian Keenaghan

11. 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 Julian Keenaghan

12. 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 Julian Keenaghan

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