Harry Powell - "Reimagining Data"
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This document discusses reimagining recommendations by measuring business similarity based on shared customers. It presents customer transaction data for Tesco, Asda, and other businesses and uses cosine similarity and matrix factorization to analyze how similar the businesses are based on which customers frequent them. It then discusses using the customer transaction data to create a graph and analyzing the graph to determine expected degrees of separation between businesses as a new recommendation metric. Finally, it outlines some of the complexity and scalability challenges with this approach and proposes solutions like factorizing graphs, eliminating insignificant paths, and linear sampling to address those challenges.
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Harry Powell - "Reimagining Data"
- 1. Reimagining Recommendations Harry Powell Digital Henley 4 May 2016.
- 2. 2 | Barclays PCB - Advanced Data Analytics Recommendation == Similarity – How is Tesco similar to Asda? – How is Tesco in Bristol similar to Asda in Bath? – How is Tesco in Bristol similar to Bristol Angling Centre? Assume that the similarity of two businesses can be measured by whether they share customers
- 3. 3 | Barclays PCB - Advanced Data Analytics The data: Customer transactions Timestam p Customer Business Amount (£) … Bob Smith Tesco, Bristol … … Mary Jones Tesco, Bristol … … Bob Smith Asda, Bath … … John Taylor Bristol Angling Centre …
- 4. 4 | Barclays PCB - Advanced Data Analytics Cosine Similarity 𝐴𝑠𝑑𝑎 = 𝐀 = (1,1,1,0,1,1,0,0) 𝑇𝑒𝑠𝑐𝑜 = 𝐁 = 1,0,1,0,1,1,0,1 𝐀 ⋅ 𝐁 = 𝐀 𝐁 cos 𝜃 Customer Tesco Asda Bob Smith Yes Yes Mary Jones No Yes John Taylor Yes Yes Jane Williams No No Gary Brown Yes Yes Liz Davis Yes Yes David Evans No No Helen Wilson Yes No
- 5. 5 | Barclays PCB - Advanced Data Analytics Matrix factorisation ⇒ 1 0 1 0 1 1 0 1 1 1 0 1 1 0 1 0 ≈ … … … … … … … … × … … Customer Tesco Asda Bob Smith Yes Yes Mary Jones No Yes John Taylor Yes Yes Jane Williams No No Gary Brown Yes Yes Liz Davis Yes Yes David Evans No No Helen Wilson Yes No Preferences of Customers Attributes of Businesses
- 6. 6 | Barclays PCB - Advanced Data Analytics Using the whole graph Tesco Asda BP Boots Timestam p CustomerI D MerchantName Amount (£) … 1 Tesco … … 1 Asda … … 2 Boots … … 2 BP … … 3 Tesco … … 3 Boots … … 3 BP … … 4 Asda …
- 7. 7 | Barclays PCB - Advanced Data Analytics Customer transactions as a graph Tesco Asda BP BootsTesco Asda BP Boots Tesco Asda BP Boots
- 8. 8 | Barclays PCB - Advanced Data Analytics Tesco Asda BP Boots Tesco Asda BP Boots Our metric: Expected Degrees of Separation
- 9. 9 | Barclays PCB - Advanced Data Analytics Complexity
- 10. 10 | Barclays PCB - Advanced Data Analytics Markov factorisation Factorise Factorise Factorise
- 11. 11 | Barclays PCB - Advanced Data Analytics Scalability problems with trees Where 𝑛 = 1,000,000 Actually 𝑛 starts at a few thousand for low 𝑘, but quickly escalates 𝑛 𝑠𝑜𝑢𝑟𝑐𝑒 𝑛𝑜𝑑𝑒𝑠 × (𝑛 − 1) 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑛𝑜𝑑𝑒𝑠 × 𝑘 𝑑𝑒𝑝𝑡ℎ × (𝑛 − 1) 𝑛𝑜𝑑𝑒𝑠 𝑎𝑡 𝑒𝑎𝑐ℎ 𝑠𝑡𝑒𝑝 ⇒ 𝑂 𝑛3 𝑘
- 12. 12 | Barclays PCB - Advanced Data Analytics Factorising graphs (recursion, A*)
- 13. 13 | Barclays PCB - Advanced Data Analytics Elimination of insignificant paths
- 14. 14 | Barclays PCB - Advanced Data Analytics Linear Sampling ≈
- 15. 15 | Barclays PCB - Advanced Data Analytics DataScienceisreimaginingdata
Editor's Notes
- Introduction to Barclays Advanced Data Analytics (ADA) ADA is a data science team which innovates, designs and builds applications that deliver, direct to customers, relevant analytical content that will help them make smart decisions to improve their lives. We are aim to make applications that will revolutionise the way Barclays relates to our customers. The long term vision is to give each of our customers the same level of engagement and support in planning their finances and their lives as they would have if they were billionaires.