Structure, Personalization, Scale: A Deep Dive into LinkedIn Search

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1 Asif Daniel Structure, Personalization, Scale: A Deep Dive into LinkedIn Search

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Overview What is LinkedIn search and why should you care? What are our systems challenges? What are our relevance challenges? 2

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Search helps members find and be found. 5

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Search for people, jobs, groups, and more. 6

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A separate product for recruiters. 8

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Search is the core of key LinkedIn use cases. 9

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What’s unique Personalized Part of a larger product experience Many products Big part Task-centric Find a job, hire top talent, find a person, … 10

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Systems Challenges 11

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Evolution of LinkedIn’ Search Architecture 2004: No Search Engine Iterate through your network and filter 12

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13 Lucene Lucene Lucene Lucene (Single Shard) Updates Queries Results 2007: Introducing Lucene (single shard, multiple replicas)

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14 Lucene Lucene Lucene Updates Queries Results Updater Updater Lucene Zoie 2008: Zoie - real-time search (search without commits/shutdown)

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15 Lucene Lucene Lucene Source 1 Queries Results Updater Updater Lucene Zoie Source 2 …. Source N Content Store …. 2008: Content Store (aggregating multiple input sources)

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16 Source 1 Queries Results Updater Updater Source 2 …. Source N Content Store …. Sharded Broker 2008: Sharded search

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17 Source 1 Queries Results Updater Updater Source 2 …. Source N Content Store …. Sensei Broker Lucene Zoie Bobo 2009: Bobo – Faceted Search

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18 Updater Updater 2010: SenseiDB (cluster management, new query language, wrapping existing pieces)

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19 Updater Updater 2011: Cleo (instant typeahead results)

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20 Updater Updater 2013: Too many stacks Group Search Article/Post Search And more…

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Challenges Index rebuilding very difficult Live updates are at an entity granularity Scoring is inflexible Lucene limitations Fragmentation – too many components, too many stacks Economic Graph 21 Opportunity

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22 Updater Updater 2014: Introducing Galene

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Life of a Query 23 Query Rewriter/ Planner Results Merging User Query Search Results Search Shard Search Shard

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Life of a Query – Within A Search Shard 24 Rewritten Query Top Results From Shard

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Life of a Query – Within A Rewriter 25

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Improvements Regular full index builds using Hadoop Easier to reshard, add fields Improved Relevance Offline relevance, query rewriting frameworks Partial Live Updates Support Allows efficient updates of high frequency fields (no sync) Goodbye Content Store, Goodbye Zoie Early termination Ultra low latency for instant results Goodbye Cleo Indexing and searching across graph entities/attributes Single engine, single stack 27

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Galene Deep dive 28

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Primer on Search 29

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Lucene An open source API that supports search functionality: Add new documents to index Delete documents from the index Construct queries Search the index using the query Score the retrieved documents 30

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The Search Index Inverted Index: Mapping from (search) terms to list of documents (they are present in) Forward Index: Mapping from documents to metadata about them 31

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The Search Index The lists are called posting lists Upto hundreds of millions of posting lists Upto hundreds of millions of documents Posting lists may contain as few as a single hit and as many as tens of millions of hits Terms can be words in the document inferred attributes about the document 33

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Lucene Queries term:“asif makhani” term:asif term:daniel +term:daniel +prefix:tunk +asif +linkedIn +term:daniel connection:50510 +term:daniel industry:software connection:50510^4 34

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Early termination We order documents in the index based on a static rank – from most important to least important An offline relevance algorithm assigns a static rank to each document on which the sorting is performed This allows retrieval to be early-terminated (assuming a strong correlation between static rank and importance of result for a specific query) Also works well with personalized search +term:asif +prefix:makh +(connection:35176 connection:418001 connection:1520032) 35

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Partial Updates Lucene segments are “document-partitioned” We have enhanced Lucene with “term-partitioned” segments We use 3 term-partitioned segments: Base index (never changed) Live update buffer Snapshot index 36

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37 Base Index Snapshot Index Live Update Buffer

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Going Forward Consolidation across verticals Improved Relevance Support Machine-learned models, query rewriting, relevant snippets,… Improved Performance Search as a Service (SeaS) Exploring the Economic Graph 38

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Quality Challenges 39

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The Search Quality Pipeline 40 spellcheck query tagging vertical intent query expansion

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Spellcheck 41 PEOPLE NAMES COMPANIES TITLES PAST QUERIES n-grams marissa => ma ar ri is ss sa metaphone mark/marc => MRK co-occurrence counts marissa:mayer = 1000 marisa meyer yahoo marissa marisa meyer mayer yahoo

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Query Tagging 42 machine learning data scientist brooklyn

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Vertical Intent: Results Blending 43 [company] [employees] [jobs] [name search]

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Vertical Intent: Typeahead 44 P(mongodb | mon) = 5% P(monsanto | mons): 50% P(mongodb | mong): 80%

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Query Expansion 45

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Ranking 46

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Ranking is highly personalized. 47

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Not just for name search. 48

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Relevance Model 49

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Examples of Features 50 Search keywords matching title = 3 Searcher location = Result location Searcher network distance to result = 2 …

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Model Training: Traditional Approach 51

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Model Training: LinkedIn’s Approach 52

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Fair Pairs and Easy Negatives 53 Sample negatives from bottom results But watch out for variable length result sets. Compromise, e.g., sample from page 10.

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Model Selection Select model based on user and query features. e.g., person name queries, recruiters making skills queries Resulting model is a tree with logistic regression leaves. Only one regression model evaluated for each document. 54

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Summary What is LinkedIn search and why should you care? LinkedIn search enables the participants in the economic graph to find and be found. What are our systems challenges? Indexing rich, structured content; retrieving using global and social factors; real-time updates. What are our relevance challenges? Query understanding, personalized machine-learned ranking models. 55

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56 Asif Makhani Daniel Tunkelang [email protected] [email protected]