The basics of recommendations systems as found implemented on traditional ecommerce sites like Amazon

1. Recommendation Systems
A Primer
Glyn Darkin - @glyndarkin

2. Types
 Collaborative Filtering
 X is frequently bought together with Y
 Customers often buy Y after looking at X
 Customer who bought X item also bought Y
 Content based
 Buy X because it has a relationship with Y
 Like Robbie Williams, try Take That
Glyn Darkin - @glyndarkin

3. Context
 User
 Recommendation is determined based on user context
 We recommend X product because you purchased Y
 Amazon’s
 Today’s Recommendations For You
 Product
 Recommendation is determined based on product context
 We recommend X product because other’s purchased Y
 Amazon’s
 Frequently Bought Together
Glyn Darkin - @glyndarkin

4. Collaborative Filtering
 Focus on statistical analysis of the relationship
between products and people
 No knowledge of product domain required in
analysis
 Technology of note
 R
 statistics programming language
 Mahout
 Machine learning and data mining
 Standard Amazon Recommendations
Glyn Darkin - @glyndarkin

5. Content Based
 Focus on product graph and defining the
relationships between products
 Some domain knowledge of the products is required
 Dependency on quality external metadata
 If you want to cross sell red house hold products you will
need a good data source to provide it
 Technology of note
 Neo4J
 Graph database
 Lucene / SolR
 Full text search
 Basic More episodes recommendation on the BBC
iPlayer
Glyn Darkin - @glyndarkin

6. Summary
 Both Collaborative & Content based
recommendations can be of a user or product
context.
 Context is important as it defines the schema of data
capture required to deliver the recommendation
 The sweet spot is probably in a hybrid approach to a
recommendation
 We must not forget the Social recommendation
where a 3rd party body of trust recommends a
product
Glyn Darkin - @glyndarkin

7. Delivery Mechanism
 Targeted
 Email – could be either User or Product Context
 Tweet – should be User Context
 Personalised Homepage
 Product page cross sell/upsell
 Landing page merchandising
 The delivery mechanism dictates the type of
technology required
Glyn Darkin - @glyndarkin

8. Data Capture Techniques
 Batched export
 Orders/Baskets
 People
 Product metadata
 Real-time – Analytics packages
 Pages
 Transactions
 Customer interaction
 Customer Surveys
 Not everybody will be able to capture
everything, therefore there maybe technology
requirements to capture particular data points
Glyn Darkin - @glyndarkin

9. Trends in recommendations
 Amazon started recommendations trend
 Everybody is tired of getting recommended products
that are not relevant to them caused by gifting or
one-off purchases
 Upsurge in “Curated” sites
 www.Etsy.com
 www.shoedazzle.com
Glyn Darkin - @glyndarkin