Recommendations

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

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Recommendations

  1. 1. Recommendation Systems A Primer Glyn Darkin - @glyndarkin
  2. 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. 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. 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. 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. 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. 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. 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. 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

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