How to do recommendations with elastic search

2. Hi,
I’m Omri Fima
• Tech Lead at
• recommendations and search group.
• Maker & Hacker

4. Not only for search.

10. Implementing
recommendation systems
with ElasticSearch

11. Why would you like to do such thing?

12. What is a search engine?
Give me the most relevant content

13. In relevance to a ‘free text query’

14. What is a recommendation system?
Give me the most relevant content

15. In relevance to a ‘user’ and a ‘context’

16. SEARCHCOMMENDATIONS

17. Why elastic search for recommendations?
• Developer understand search
• Ranking
• Aggregations
• Real-time

18. What can you do with elastic search?
• Collaborative Filtering
• Anomaly detection
• More like this
• Trends
https://qbox.io/blog/building-simple-recommender-systems-for-elasticsearch-1

19. Today we are going to
focus on content-based
recommender engine

21. model content for recommendations
1

22. The building blocks of a
content-based recommender engine
Story Feed

23. Story

24. Story

25. Story – structure
• Content
• Metadata
• Type
• Timestamp
• Author
• Social Indicators
• Likes, shares, Comments.
• Quality Indicators
• Images
• Clicks
• Source Quality
• Timeliness
• Feeds

27. likes
1
Actor
Shani
Views
844
Comments
0
Type
Product
Time
17/5/2017

28. likes
1
Actor
Shani
Views
844
Comments
0
Type
Product
Time
17/5/2017
Category
Running Shoes
Brand
Brooks
Catalog
For Running
Country
Israel
Category
Shoes
Blue
Running Shoes
1.0
1.5
0.5
0.2
0.1
2.0

29. PUT stories/story/123
{
storyId:”123”,
content:”something about shoes”,
sources:[
{feedId:”Running Shoes”,score: 0.8},
{feedId:”Brooks”,score: 0.2}
]],
Boosts:{
likes:1,
views:220,
},
actor: 93894,
timestamp:1496900493,
storyType:”text”
}

30. Writing ranking scripts
2

31. How elastic search scoring
works?

32. we are going to ignore it.

33. CODE
"query": {
"function_score": {
"query": { match_all: {}},
"functions": [{
"script_score": {
"script": {
“script": “Put magic script here",
"params": {
...
}

34. We want to recommend
our Best content

35. Quality indicators
Content type (video, text, product, deal)
Creator quality
CTR
Clickbaitiness Score

36. Quality indicators
doc[‘ctr'].value * ctr_weight +
story_weights.get(doc[‘type'].value) * story_type_weight +
doc[‘actor_score'].value * actor_quality_weight

37. Social indicators
Likes
Comments
Shares

38. Social indicators
doc['comments'].value * comments_weight +
doc['likes'].value * likes_weight +
doc['shares'].value * shares_weight

39. Social indicators
Rich will get richer.
Bots.
Vote brigades.

40. Social indicators
Log rule – the first 10 likes worth as next 100 worth as next 1000.
5 likes = 0.69
50 likes = 1.69
500 likes = 2.69
5000 likes = 3.69
https://medium.com/hacking-and-gonzo/how-reddit-ranking-
algorithms-work-ef111e33d0d9

41. Social indicators
log10(doc['comments'].value+1) * comments_weight +
log10(doc['likes'].value+1) * likes_weight +
log10(doc['shares'].value+1) * shares_weight

42. We now recommend our
Best content.

43. We now recommend our
Best content ever.

44. Which is a problem

45. Timeliness

46. Decay

47. Time
weight * exp(-1 * decayRate * (now-doc[ 'datePublished'].value))

48. Time
DECAY IN INDEX TIME VS. QUERY TIME.
“score won’t decrease as time goes
by, but newer stories will get a
higher score than older.”

49. Variant Decay Functions
Change decay rate according to visit frequency.
• Visit once a week, and don’t miss high quality stories from 5 days ago.
• Visit every 15 minutes, and see always fresh content.

50. We now recommend
High-Quality & Fresh content.

51. But we are still missing
something.

52. High quality &
Fresh &
Great Reviews &
Great location

55. Personalization

56. Brand
Star Wars
Topic
Reading
Brand
LEGO
Country
Israel
Category
Shoes
Topic
Cooking
1.0
1.5
0.5
0.2
0.1
2.0
Topic
Arts & Crafts
Topic
Software
Category
Woodworking
Person
Ofer Egozi
Brand
LG
2.6
1.1
1.7
0.3
1.3

57. CODE
"query": {
"nested": {
"path": "sources"
"query": {
"terms": {
"sources.feed": ["T761", "T354 ", "T798", "T978"]
}
}
}
}

58. Brand
Star Wars
Brand
LEGO 1.11.5
Demographic
11-14
Price
Medium1.1
Brand
Star Wars 2.0
Brand
LEGO 2.0
Price
Medium
x x x x
User
Interests
Story
Relevancy
= = = =
3.0 2.2 0 5.2+ + +Story Score

59. CODE
"query": {
"nested": {
"query": {
"function_score": {
"query": {
"terms": {
"sources.feed": ["T761", "T354 ", "T798", "T978"
}
},
"functions": [{
"script_score": {
"script": {
"inline": "NewsfeedScorerFeeds",

60. …
"functions": [{
"script_score": {
"script": {
“script": "interests.get(doc['feed'].value.toString())*(1+doc['s'].value)”
"params": {
"interests": { " T761": 2.0, " T354 ": 5.6, " T798 ": 1.03," T978 ": 0.2}
}
}
}
}],
"boost_mode": "replace"
}
},

61. …
"functions": [{
"script_score": {
"script": {
“script": "interests.get(doc['feed'].value.toString())*(1+doc['s'].value)”
"params": {
"interests": { " T761": 2.0, " T354 ": 5.6, " T798 ": 1.03," T978 ": 0.2}
}
}
}
}],
"boost_mode": "replace"
}

62. …
"functions": [{
"script_score": {
"script": {
“script": "interests.get(doc['feed'].value.toString())*(1+doc['s'].value)”
"params": {
"interests": { " T761": 2.0, " T354 ": 5.6, " T798 ": 1.03," T978 ": 0.2}
}
}
}
}],
"boost_mode": "replace"
}

63. CODE
"query": {
"function_score": {
"query": { match_all: {}},
"functions": [{
"script_score": {
"script": {
“script": “Put magic script here",
"params": {
...
}

64. CODE – let’s talk about the magic script
Score =_score+______________+_____________+______________
We got it in the previous stage
Quality Score
Relevancy Score
Social Score Timeliness Score

65. All together now
Story score =
SocialScore+QualityScore+(ΣFeedsScore)
How Facebook rank their content
https://techcrunch.com/2016/09/06/ultimate-guide-to-the-news-feed/

66. Advanced topics
3

67. Relevancy considerations.
• How to choose your weights?
• How to measure your relevancy?

68. Relevancy considerations.
•Quantitative vs. Qualitative KPI’s
•Mechanical Turk User testing
•AB Testing
•Benchmarks
•ML - https://github.com/o19s/elasticsearch-learning-to-rank

69. Relevancy considerations.
•Quantitative vs. Qualitative KPI’s
•Accuracy
•Diversity
•Novelty
•Serendipity
https://gab41.lab41.org/recommender-systems-its-not-all-about-the-accuracy-562c7dceeaff

70. Performance considerations.
• Index time vs. query time
• Score vs filter.
• How to cache?
• Native scripts vs interpreted scripts.

71. Thank You