This document provides an overview and agenda for a presentation on basic recommendation engines using AgensGraph. It discusses recommendation systems and how they have led to increased sales and engagement on platforms like Amazon, Netflix, and YouTube. It then covers collaborative filtering and content-based filtering approaches. The presentation demonstrates building a recommendation engine by importing rating data into AgensGraph, creating graph structures to represent ratings and similarities between users, and using those to create recommendation edges. It concludes with examples of further enhancements like considering centrality and categorization of products, as well as business factors like inventory levels and margins.

1. ⓒ 2017 by Bitnine Co, Ltd. All Rights Reserved. Bitnine Global Company ConfidentialBitnine Global Company Confidential
May 2018Joe Fagan
Snr Dir Presales EMEA
www.bitnine.net
www.bitnine.net
Basic Recommendation Engine

2. AGENDA – AgensGraph Introduction
Agenda
Introduction to Recommendation Engines
Collaborative based filtering approach
Some arithmetic
Importing data into AgensGraph
Creating graph, ratings and similarity edges
Creating recommendations edges
Further enhancements to recommendation Engine.

3. What is an RS?
Outcome:
 15%-35% increase in sales
 30%-80% increase in screen time
Greater ROI than any other investment
A Recommendation System (RS) finds, for each user, the
‘product’ most likely to be relevant.
Playlist: •Youtube, Netflix
Content: •Facebook, Twitter
People: •LinkedIn, Badoo
Product: •Amazon, Alibaba
The first RS was implemented in 1994. Today 35% of purchases on Amazon and
75% of what we watch on Netflix comes from RSs. Source
Alternative Outcome: Polarisation

4. Analytics Journey.
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Descriptive
Analytics
Sum, Avg,
Patterns
Sell more
hats in winter
Diagnostic
Analytics
Correlations
and
reasoning
Cold = more
hats
Predictive
Analytics
Describe the
future
Next winter =
more hats
Prescriptive
Analytics
Write the
future
Make it
colder ->
more hats
- $ $$$ $$$
Value to business
RS lives somewhere
between predictive
and prescriptive
analytics

5. 2 basic principles
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Alice and Bob buy blue and green
Alice buys yellow
Recommend yellow to Bob
Bob buys blue
Blue is similar to yellow
=> Recommend yellow to Bob
Collaborative Filtering Content Based Filtering
Alice Bob
Similar
Similar
Bob

6. 0.5x2 + 0.2x4 + 0.6x5 / .5+.2+.6 = 4.8 / 1.3 = 3.7
Ratings Matrix and Similarity
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P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
C1 3 4 2 4 3 5 3 2
C2 5 2 1 2 5 3
C3 1 5 1
C4 1 2 ?? 3 2 5 3 3
C5 4 1 2 5 2
C6 5 4 5 1
C7 4 5 5 3 5 4 1 4
C8 1 3 1 5 4 2 4 5
A ratings matrix is the starting point.
The ratings can be explicit (actual ratings) or implicit (purchases, frequency of purchases,
timeline)
The challenge is to predict all the blank entries. EG How will C4 feel about P3?
To answer this we consider how people similar to C4 felt about P3.
There are many ways to calculate similarity. Accepted standard is cosine similarity (next slide)
Ratings: Cust, Product
Similarity: Cust, Cust
C1 C2 C3 C4 C5 C6 C7 C8
C1 0.7 0.6 0.5 0.4 0.4 0.6 0.6
C2 0.7 0.2 0.3 0.6 0.5 0.6 0.5
C3 0.6 0.2 0.2 0.2 0.2 0.1
C4 0.5 0.3 0.2 0.2 0.7 0.6 0.8
C5 0.4 0.6 0.2 0.3 0.6 0.6
C6 0.4 0.5 0.2 0.7 0.3 0.5 0.6
C7 0.6 0.6 0.2 0.6 0.6 0.5 0.6
C8 0.6 0.5 0.1 0.8 0.6 0.6 0.6
Once we know similarity, we want to favour the ratings of similar people and diminish the
influence of dissimilar people.
C5C1 C7
Once all missing ratings have been calculated for each customer, we pick the highest
calculated rating and recommend that product.

7. Cosine similarity
To measure similarity, place the customers as vectors in a vector space whose co-
ordinates are the ratings of the products.
Look at 2 vectors in just 3d (really, we’re in the dimensional space of the number of
products)
Similar customers point in similar directions. Measure the cosine of the angle
between the vectors.
Identical = Cos(0)=1, Orthogonal = Cos(90) = 0
Similarity = Cos(theta) = 3*5 + 4*0 + 2*0 / 5.4*5 = 0.56
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1 2 3
P1 P2 P3 Len
A C1 3 4 2 5.4
B C2 5 0 0 5
= 0.56
For some mind-blowing graphics and explanation of topics in linear algebra on Youtube search 3Blue1Brown Linear Algebra

8. Cypher ASCII Art
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cust
buys
prod
( ) - [ ] -> ()
( :cust ) - [ :buys ] -> ( :prod )
( xx :cust ) - [ r :buys ] -> ( y :prod )
( xx :cust {name: “C1” } ) - [ r :buys { qty: 4 } ] -> ( y :prod { p: “P1” } )
Base notation
Labels
Identifiers
Properties and Values

9. AgesnBrowser images
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Meta Graph SimilarityRates

10. Further improvements
Use Cosine centrality on ratings
Avoids no rating = negative rating
Smooths harsh and easy raters
Categorize products
Calculate similarity by category
For consumables show products already purchased
For Books/Music show similar products
Factor in business preference for vendors/products/categories
Based on margin, stock, return rates, strategic direction
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P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
C1 -0.3 0.8 -1.3 0.8 -0.3 1.8 -0.3 -1.3
C2 2.0 -1.0 -2.0 -1.0 2.0
C3 -1.3 2.7 -1.3
C4 -1.7 -0.7 0.3 -0.7 2.3 0.3 0.3
C5 1.2 -1.8 -0.8 2.2 -0.8
C6 1.3 0.3 1.3 -2.8
C7 0.1 1.1 1.1 -0.9 1.1 0.1 -2.9 0.1
C8 -2.1 -0.1 -2.1 1.9 0.9 -1.1 0.9 1.9
Cosine Centrality ratings
F1
F2
F3
K1
K2
K3
P1
rec
Rec
Final
Product Factor
(Held in vertex)
Business Direction
Scaling factors
Adjustment to
recommendation
Final
recommendation
Example: F1 is stock, F2 is margin
• Business objective says reduce stock is
priority = Increase K1, reduce K2.
• Business says margin is #1 priority = reduce
K1, increase K2
• With enough data and A/B testing adjusting
K’s becomes a Machine Learning problem.
stock
margin

11. 11
Thank you for your attention