Extending Solr: Building a Cloud-like Knowledge Discovery Platform

Extending Solr: Building a Cloud-like Knowledge Discovery Platform Trey Grainger , ,CareerBuilder

Overview CareerBuilder’s Cloud-like Knowledge Discovery Platform Scalable approaches to multi-lingual text analysis (with research study) Multiple fields vs Multiple Cores vs Single Field Custom Scoring Payloads and on-the-fly bucket scoring Implementing a keyword spamming penalty Solr as a Cloud Service Scalable, customizable search for everybody Knowledge Discovery & Data Analytics

My background Trey Grainger Search Technology Development Team Lead @ CareerBuilder.com Relevant Background: Search & Recommendations High-volume, N-tier Architectures NLP, Relevancy Tuning, user group testing & machine learning Fun Side Project: Founder and Site Architect @ Celiaccess.com

CareerBuilder’s Search Scale Over 1 million new jobs each month Over 40 million resumes ~150 globally distributed search servers (in the U.S., Europe, & Asia) Several thousand unique, dynamically generated indexes Over a million searches an hour >100 Million Search Documents

Recommendations We classify all content (Jobs, Resumes, etc.) and index the classified content into Solr We use a combination of collaborative filtering and classification techniques We utilize a custom scorer and payloads to apply higher bucket weights to more relevant content Recommendations are real-time and largely driven by search

Multi-lingual Analysis Approach 1: Different Field Per Language Advantages: Simple, easiest to implement Disadvantages: My require keeping duplicate copies of your text per language If searching across each field (dismax style), slows search down, especially if handling many languages Approach 2: Different Solr Core per language Each core has your field defined with a different Analyzer chain specific to that core’s language Advantages: Searching can be completely language-agnostic and additional overhead to search more languages simultaneously is negligible Disadvantages : Multi-lingual documents require indexing to multiple cores, potentially messing up relevancy and adding complexity Have to write your own language-dependent sharding If you don’t already have distributed search, this adds complexity and overhead

Multi-lingual Analysis Approach 3: All languages in one field Advantages: Only one field needed regardless of number of languages Avoids a field explosion or a Solr core explosion as you scale to handle more languages Disadvantages: Can end up with some “noise” in the index if you process most text in lots of languages (especially if stemming and not lemmatizing) Currently requires writing your own Tokenizer or Filter Strategy: 1) Copy token stream and create a stemmer/lemmatizer for each language 2) Pass the original into each stemmer/lemmatizer 3) Stack the outputs of each stemmer/lemmatizer Input: Output:

Multi-lingual Analysis Case Study: Stemming vs. Lemmatization Example: dries >> dri vs dries >> dry Take-away : Lemmatization allows you to greatly increase recall while preserving the precision you lose with stemming i.e. English shows 92% increase in recall using Lemmatization with minimal impact on precision Measuring Recall Overlap Between Options

Custom Scoring Search Terms can be boosted differently: q=web^2 development^5 AND jobtitle :(software engineer)^10 Some Fields can be weighted (scored) higher than others i.e. Field1 ^10, Field2 ^5, Field3 ^2, Field 4 ^.01 Content within Fields can be boosted differently design [ 1 ] / engineer [ 1 ] / really [ ] / great [ ] / job [ ] / ten[ 3 ] / years[ 3 ] / experience[ 3 ] / careerbuilder [ 2 ] / design [ 2 ], … Field1 : bucket=[1] boost= 10; Field2 : bucket=[2] boost=1.5; Field3 : bucket=[] weight= 1; Field4 : bucket=3 weight=1.5 We can pass in a parameter to solr at query time specifying the boost to apply to each bucket i.e. …&bucketWeights=1:10;2:1.5;3:1.5 You can also do index-time boosting, but this reduces your ability to do query-side relevancy experiments and requires norms to always be on By making all scoring parameters overridable at query time, we are able to do A / B testing to consistently improve our relevancy model

Stopping Keyword Spamming We already subclass PayloadTermQuery and tie in custom scoring for our buckets weights For each payload “bucket” (or across all buckets), we can count the number of hits and penalize the score if a particular keyword appears too many times Payload scoring then essentially becomes BucketBoost( payloadBucket ) * HitMap( #hitsPerbucket ) By adjusting our HitMap function, we can thus generate any kind of relevancy curve for how much each additional term adds to (or subtracts from) the relevancy score for that document ex: Bell curve, Linear, Bi-linear, Linear with drop-off, custom map, etc.

CareerBuilder’s Search Cloud Goals: Make search easy to use and accessible to all engineers (not just the search team) Allow schema changes without mucking with solr (on hundreds of servers) Make solr installs generic and independent of any particular implementation

Creating a virtual search engine 3 Main Cloud Actions: Index, Search, Delete

Creating a virtual search engine Creating a Schema

Creating a virtual search engine Creating a Document Processing Results A QueryResult object comes back from the SearchEngine.Search method with all of the main types (search records, facets, meta info, etc) parsed out into objects Behind the Scenes: We have a distributed architecture handling queuing all documents to appropriate datacenters, feeding the clusters, and load-balancing searches between all available clusters for the given search pool.

Knowledge Discovery & Data Analytics

Clustering: Hyperion Developer

Take Aways Know how your linguistics affect precision and recall and choose wisely; know how to tweak for your domain. A flexible software api that turn Solr into a SAAS type cloud app can greatly increase agility and adoption of search. Search isn’t just about finding and navigating content… it can be used to learn from and create it, as well.

Contact Trey Grainger [email_address] http://www.careerbuilder.com

Extending Solr: Building a Cloud-like Knowledge Discovery Platform

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Extending Solr: Building a Cloud-like Knowledge Discovery Platform