The document discusses using a NoSQL and Kafka solution for recommendation problems. It involves using a document-oriented database, Kafka connectors to ingest and process data, and building a graph to model products and user reviews. Recommendations are generated for users based on their reviews, other users' reviews of similar products, and the products' attributes and categories. The results are ranked and sorted before being returned to users.

1. Recommendation problem:
NoSQL and Kafka solution
Alberto De Lazzari
@albertodela80
Rome, May 18, 2018

2. Recommendation
A suggestion or proposal as to the best
course of action, especially one put
forward by an authoritative body.

3. Recommendation Today

4. Alcohol Platform

5. Alcohol Reco

6. The Products

7. Discrete Values
Beer
Whisky
Rum
Spirits
Single Malt
Pale Ale
Red
Scottish

8. Continuous Values
Width
Weight
Rating
0 ∞
0 ∞153
21
0 53.1

9. Document Oriented Database

10. Document - Key
Strongbow174 Gold
Apple Hard Cider - 6pk /
11.2oz Bottles
AV--c-GoYSSHbkXwp16b

11. Document - Value

12. The Pipes
Kafka Connector

13. The Graph

14. The Graph - Multidimensional Approach
Wine Omar
:REVIEWS
Beer
:REVIEWS
Jim
:REVIEWS
Which User?
Which
Product?

15. The Graph - Hard Relationships
"weight": {
"value": 7.5,
"unit": "lbs"
}
Product Weight
:HAS_WEIGHT

16. The Graph - Soft Relationships
MATCH (a:Weight),(b:Weight)
WHERE id(a)<id(b)
WITH a,b,
apoc.algo.euclideanDistance([a.value],[b.value])
AS dist
WHERE dist <= 0.25
MERGE (a)-[:SIMILAR {value: dist}]->(b)
Weight
7.6
:SIMILAR
Weight
7.5
{value: 0.09999999999999964}

17. The Graph - Data Model

18. The Graph - Data Ingestion
Kafka Connector
doc2graph
Cypher
Core

19. The Graph - Data Processing
Kafka Connector
doc2graph
Cypher
Core
Replica
ML
Graph
Events
Bolt
SoftRels

20. The Index

21. The Index - Data Ingestion
Kafka Connector
doc2graph
Cypher
Core
Replica
ML
Graph
Events
Bolt
SoftRels
Index

22. Recommendations per User
The current User

23. Recommendations per User
The current User did some
good reviews to Products.

24. Recommendations per User
The current User did some
good reviews to Products.
The Product was reviewed
by another User.

25. Recommendations per User
The current User did some
good reviews to Products.
The Product was reviewed
by another User.
This one reviewed other
Products.

26. Recommendations per User
The current User did some
good reviews to Products.
The Product was reviewed
by another User.
This one reviewed other
Products.
This Product is similar to
other ones.

27. Recommendations per User
The current User did some
good reviews to Products.
The Product was reviewed
by another User.
This one reviewed other
Products.
This Product is similar to
other ones.
And can have some
categories in common with
the previous ones.

28. Clustering

29. Ranking
cat_1 cat_2 cat_3 cat_4
0.0 0.6 0.3 0.1
0.5 0.4 0.1 0.0
0.7 0.3 0.0 0.0
SORTING
FOR
USER
SEARCHING
RESULTS

30. Elastic Update
curl -XPOST 'localhost:9200/alcohol/_doc/_bulk?pretty&pretty'
-H 'Content-Type: application/json'
-d'
{"update":{"_id":"AV--c-GoYSSHbkXwp16b"}}
{"doc": {"ranking": {
"cat_1": 0,
"cat_2": 0.6,
"cat_3": 0.3,
"cat_4": 0.1}}}
'

31. Sorting the results of Searching
curl -XGET 'localhost:9200/alcohol/_search?pretty'
-H 'Content-Type: application/json'
-d'
{
"query": {
"fuzzy" : { "name" : "Wine" }
},
"_source": ["brand", "name"],
"sort": [ {"ranking.cat_2" : {"order" : "asc"}}]
}
'

32. Conclusion
Join the power
of all the NoSQL technologies
to get the best from your data

33. Answers
Questions