Implementing keyword sorting in Elasticsearch allows prioritizing property search results based on how many search keywords are found in property descriptions. The analysis shows that users searching with keywords view more pages per session. However, fuzzy matching can result in false positives when similar words are matched. Going forward, alternatives to improve precision for fuzzy matching will be explored, such as demoting fuzzy-only matches or checking if a query word is valid before applying fuzziness.

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Implementing Keyword Sort with
Elasticsearch
Andrei Nicusan
Tech Lead – Intelligent
Search

2. • Property Search facts and figures
• Prioritising search results by keywords
• A peek under the hood
• Lessons learnt
• What’s next
2
Agenda

3. 3
Property Search facts and figures

4. • ~1.3M live properties
• ~29M searches per day
• 46 milliseconds median Elasticsearch response time (incl.
network roundtrip)
• 197 milliseconds at the 99th percentile Elasticsearch response
time
• 0.001% error rate (99.999% success rate)
• 3 hot clusters in 3 geographically-separated data centres
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Property Search

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Prioritising search results by keywords

11. • There’s a lot of text in a property description, so why not?
• To increase/ improve flexibility in the way people search
• Get some insights into what people search for as a basis to
further add ”smart” into our search offering
• Allow agents to target property features to users (search
engine optimisation for Rightmove)
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Keyword sort – why?

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A peek under the hood

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A sample document

14. A sample document
Premium listing
Summary
Key features
Description

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Analysis - mapping

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Analysis chain
“No pets allowed” gets indexed as “No ~~pets allowed”
Kudos to Zachary Tong - https://www.elastic.co/blog/quick-tips-negative-
connotation-filter

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Analysis chain

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Analysis chain

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Query analysis

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Query analysis

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Query
Single words

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Query
Single words

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Query
Single words

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Query - fuzziness
• Fuzziness is expressed in terms of Levensthein-Damerau distance, i.e.
the number of changes needed to transform string A into string B,
where a change could be:
• Adding a character: detached -> detatched
• Removing a character: detached -> detaced
• Swapping two neighbouring characters: detached -> deatched
• Fuzzy matching raises the biggest concerns around false positives
• With a fuzziness setting of 2, attached would match detached

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Query
Single words

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Query – constant scoring
• Default Elasticsearch relevance algorithm (BM25):
• Term frequency: This house has a garden. The garden is big scores
higher than This house has a garden
• Inverse document frequency
• Field length norm: This house has a garden scores higher than This
house has a garden and a driveway.
• Why it doesn’t work for us:
• We want the results sorted by number of keywords found, regardless of
how many times
• Field length norm clashes with our preference for more detailed, more
complete descriptions

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Query
Single words

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Query
Phrases Putting it all together

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Query
Phrases Putting it all together

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Query
Phrases Putting it all together

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Lessons learned

32. • Most queried words: garage, garden, pool, sea view, annexe (also
annex is 9th)
• ~105K keyword searches per day
• Users who apply keywords see 37 pages per session on average
(compared to 11 for those who don’t)
• 99ms median Elastic search response time (compared to 46
overall)
32
What we know

33. • False positives, e.g. acre/care
• Fuzzy matching
• It’s hard to track when fuzzy matching was “useful”
• Most enquiries/negative feedback are related to fuzzy matching
• No one will send you a thank you note when they got some
results right when they misspelled a word
• Highlights on property cards using default highlighters
33
What went wrong

34. • We have a few alternatives to improve precision on fuzzy matching
• Go back to default fuzziness settings which skip the first few
characters of a word
• Demote results that only matched upon applying fuzziness
• Only run a fuzzy search when the query word is not an actual English
word. We’d need a third party library to tell us what’s a legit English
word.
• Improve feedback when users click through to a property’s page
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What happens next

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Q&A time