An introduction to Elasticsearch's advanced relevance ranking toolbox

An Introduction to Elasticsearch’s Advanced Relevance Ranking Toolbox
The hallmark of a great search experience is always delivering the most relevant results, quickly, to every user.
The difficulty lies behind the scenes in making that happen elegantly and at a scale.
From App Search’s intuitive drag and drop interface to the advanced relevance capabilities built into the core of
Elasticsearch — Elastic offers a range of tools for developers to tune relevance ranking and create incredible search
experiences.
In this session, we’ll explore some of Elasticsearch’s advanced relevance ranking features, such as dense vector fields,
BM25f, ranking evaluation, and more. Plus we’ll give you some ideas for how these features are being used by other
Elastic users to create world-class, category defining search experiences.
Opening suggestion:
Show poor search experience (1). Show amazing search experience (2).
Show how to achieve amazing search experience. First with App Search (quick) (3).
Then, pivot, for more complex situations or for more control / power over whatever - this is the list of features on the next slide (4).
Abstract

Key topics
• Performance - separating docs from aggs
• Ranking evaluation API
• BM25f
• Synonyms administration via API
• Distance feature query
• Script score query
• Rank_feature
• Pinned query
• Vector similarity for reranking
• Tuning params with ML LTR
I will likely need to cancel some of the slides since there’s too much here to
fit in 25 min.

3
Advanced Relevance Ranking
Gilad Gal
Product Manager, Elasticsearch
ElasticON

Agenda Slide
Use color to highlight
Performance when ranking
Relevance ranking evaluation
Specific query types and their usefulness
Vector similarity in relevance ranking
Relevance ranking research and roadmap
1
2
3
4
5

Docs first & stats separately - better performance
Separate the query that returns the results from the stats/agg query
A Z
A X
A Y
A X
B Y
B Z
B X
B Y
B X
A B Z
Top
Ranking
A Z
A X
A Y
A X
B Y
B Z
B X
B Y
B X
A B Z
Result
count
Skipping
uncompetitive
results
Full index scan
Review the results while
facets and total count load
Faster UI response where
it matters!

Docs first & stats separately - better performance
Separate the query that returns the results from the stats/agg query
If no aggregation or hit count (beyond configurable k results)
Performance impact:
Term queries: 3x to 7x faster
Conjunction (and) queries: 3% to 7x faster
Disjunction (or) queries: 8% to 15x faster
Running Lucene's benchmark suite

• Set of representative queries
– Representative ! Frequent
– Larger is better, e.g.:
– 10^3 is probably reasonable
– 10^1 is probably too little
• Set of correct results per query
– Set size per query that covers most users’ interest,
e.g. 510
– Examine more results; potentially expand the query
• Some metrics use score and “not in set”,
others require a set
!
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Tagged Data Curation
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Supported Metrics
• Precision at K
• Mean Reciprocal Rank
• Discounted Cumulative Gain

Precision at K
• Number of correct results within the
first k results divided by k
• For one query:
• For a set of |Q|queries:
Supported Metrics

Mean Reciprocal Rank
• |Q|  number of queries in test set
• Ranki
:= position of the first correct result for query i
Supported Metrics

• Cumulative Gain
– Sum of ranking scores till position P
– Problem: different sorting of records 1..P would yield the
same CGP
– Relating to
– Position within k (i indicates the position)
– Ranking score (reli:= ranking score of record in position i)
• Higher positions are more influential
• Average DCG across multiple queries
– Ideally keep the same number of results in the result set
– Issue: different queries have different impact due to
average higher/lower score
Discounted Cumulative Gain

Supported Metrics
• Precision at K -
Simple to understand
• Mean Reciprocal Rank -
First correct answer
• Discounted Cumulative
Gain - Relevance
ranking order

BM25F
The oversimplified history of term frequency algorithm
TF/IDF
Frequent terms are
less indicative than
rare terms
TF
Rank by frequency
of the query term in
the document
BM25
Adjust for lengthy
text being more
probable to contain
the query term and
limits TF impact for
common words
BM25F
Adjust for multiple
fields with varying
field importance

Combined Fields Query
• Benefit:
– Improved relevance ranking when ranking
on multiple fields
• How
– Treat as if unified into a single field
– Support boosting per field
– Use BM25F
Using BM25F

The importance of the record’s intrinsic value
• Records differ in their value, regardless of query
• Typical examples:
– URL length
– User reviews score
– Popularity
– Number of links to / citations of
– Algorithms:
• Hubs and authorities (HITS
• PageRank
Static but crucial
Pre-calculated numbers

Rank Feature Query
• New datatypes:
– rank_feature
– rank_features (vector of rank_feature variables)
• Add to the relevance ranking score
• Allow normalization prior to adding:
– Saturation
– Logarithm
– Sigmoid

Script Score Query
• Based on all fields
• Including _score
• Normalize
– Saturation
– Logarithm
– Sigmoid
• Write your own painless or use efficient
ready-made functions
Define the function for relevance ranking score

Distance Feature Query
• Benefits:
– Newer data is frequently more relevant
– Geographical proximity as a relevance indicator in
non-geo searches
• Easy to use, sophisticated under the hood:
– Designed to leverage top-k faster queries for
improved performance
– Supports normalization through Saturation
Geo and Time Proximity: closer is frequently more relevant

Result Pinning
• Pinning the highest ranking results per query
• Facilitates tailoring SERPs for frequent queries
What is it good for?

Vector Similarity
• Vector distance as similarity indicator
– Embedding for natural language processing
– Feature vectors for image analysis (using
convolutional neural networks)
• Different types of distances/similarities
used for different needs
– Dot Product
– Cosine
– Manhattan
– Euclidean
What is it good for?
x1
x2
x2048
...
rather
than this
This

Vector Manhattan & Euclidean Distances
• Euclidean:
• Manhattan:
p = [p1
, p2
, p3
, …, pn
]
q = [q1
, q2
, q3
, …, qn
]
What is it?

Vector Similarity Functions
Use deep learning models for relevance ranking
Natural Language
Processing Model
Text documents
Queries
Documents
with vectors
Text documents
Documents
Queries
Queries
a1
a2
…
an
a1
a2
…
an
a1
a2
…
an
Text
Convolutional
Neural Network
Image
Embeddings
Feature vectors
a1
a2
…
an
a1
a2
…
an
For Example:
Search by
vector similarity &
text/keywords/num
Ingest documents

Agenda
Performance when ranking
Relevance ranking evaluation
Specific query types and their usefulness
Vector similarity in relevance ranking
Relevance ranking research and roadmap
1
2
3
4
5

Vector Similarity Usage with Elasticsearch
Use deep learning models for relevance ranking
• Image search
– CNN (convolutional neural networks)
• Natural language processing
– BERT, Word2vec, GloVe
https://image-search.eden.elstc.co/home

Approximate Nearest Neighbours
Vector similarity performance and scalability
• Vector similarity as an addition to term frequency ranking works great
– Very typical situation
– Scales well
– Limit the set using term frequency
– Re-rank based on vector similarity
• ANN for performance and scalability
– Ranking only by vector similarity
– Extending the term frequency based set with vector similarity results
• Based on HNSW support in Lucene
Tentative
Roadmap
Layer 2
Layer 1
Layer 0

Embeddings Creation
Translating text into vectors
• Pre-trained deep learning model support,
e.g. for BERT
• Out of the box translation of text into
embeddings
– Docs at ingest time
– Queries at query time
• Useful for NLP
– Search by meaning
– Venture beyond bag of keywords
– E.g. for helpdesk and documentation search
Tentative
Roadmap
Source: Siobhán Grayson in Wikimedia.org -
https://commons.wikimedia.org/wiki/File:TSNE_visualisation_of_word_e
mbeddings_generated_using_19th_century_literature.pdf

Tuning Ranking With Machine Learning
Configuring ranking parameters using ranking evaluation
• Tuning relevance ranking using machine learning
– Field boosting (see “Improving search relevance with data-driven query optimization” by Josh Devins)
– BM25 parameters
– Normalization parameters
• Possible today using ranking evaluation API, but run outside
Elasticsearch
• Considered for the roadmap including a queries curation mechanism
to obtain a tagged set for training/testing/validation
Tentative
Roadmap

Summary
• Lots of mechanism
• Designed to work together
• To facilitate the relevance ranking that best suits your need
BM25F & combined_fields
query
rank_feature query
Normalization: saturation,
logarithm, or sigmoid
distance_feature query
pinned query
Ranking by vector
similarity
Precision at K Mean Reciprocal Rank
Discounted Cumulative
Gain
Embedding creation using
pre-trained models
Tuning ranking with
learning to rank
Approximate Nearest
Neighbours using HNSW

34
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Safe Harbor
Statement
This presentation includes forward-looking
statements that are subject to risks and
uncertainties. Actual results may diﬀer materially
as a result of various risk factors included in the
reports on the Forms 10-K, 10-Q, and 8-K, and in
other ﬁlings we make with the SEC from time to
time. Elastic undertakes no obligation to update
any of these forward-looking statements.

47
Safe Harbor
Statement

48
Closing slide

49
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one rogue log can ruin the
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Elastic Stack and Beats.
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3 solutions powered by 1 stack
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The Elastic Stack
Reliably and securely take data from
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Deploy anywhere.
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on Kubernetes
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Powered by
the stack
3 solutions
Deployed
anywhere

Deploy anywhere.
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Kubernetes
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job
machine
learning
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settings
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users and
roles
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data visualizer
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specialization
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stack

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members
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language
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location distributed visibility plan E commerce family vacation presentation education guide book
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training support subscription
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downloads custom consulting community community
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An introduction to Elasticsearch's advanced relevance ranking toolbox

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An introduction to Elasticsearch's advanced relevance ranking toolbox