Building a real time, solr-powered recommendation engine

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Searching text is what Solr is known for, but did you know that many companies receive an equal or greater business impact through implementing a recommendation engine in addition to their text search capabilities? With a few tweaks, Solr (or Lucene) can also serve as a full featured recommendation engine. Machine learning libraries like Apache Mahout provide excellent behavior-based, off-line recommendation algorithms, but what if you want more control? This talk will demonstrate how to effectively utilize Solr to perform collaborative filtering (users who liked this also liked…), categorical classification and subsequent hierarchical-based recommendations, as well as related-concept extraction and concept based recommendations. Sound difficult? It’s not. Come learn step-by-step how to create a powerful real-time recommendation engine using Apache Solr and see some real-world examples of some of these strategies in action.

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  • This is great!! Thank you so much
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  • SIR CAN U PLEASE EXPLAIN HOE IT WORKS WITH TOURISM OR TRAVEL DOMAIN
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  • I would guess this method is less accurate than finding most similar users who have much more attributes to compute with, then output articles that are visited by most similar users. Because , despite inverted index used insde lucene, lucene fundamentally still calculates similarity with cosine algorithm which tends to be more accurate if more attributes are input into formula. However, I haven't tried this method yet, this is just my opinion. Has anyone tested both approaches yet? In fact, I think this can be done within in rational database which eliminates the process of using lucene but outcome is same
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  • What is the difference between performing content based filtering using apache lucene/solr and standard content-based recommendation algorithms(clustering, classification)?
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  • What about use not-text information for clustering? Education level, Employment Type for example. It is important for some jobs and no-matter for other.
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Building a real time, solr-powered recommendation engine

  1. 1. Building a Real-time, Solr-powered Recommendation Engine Trey Grainger Manager, Search Technology Development @Lucene Revolution 2012 - Boston
  2. 2. Overview• Overview of Search & Matching Concepts• Recommendation Approaches in Solr: • Attribute-based • Hierarchical Classification • Concept-based • More-like-this • Collaborative Filtering • Hybrid Approaches• Important Considerations & Advanced Capabilities @ CareerBuilder
  3. 3. My BackgroundTrey Grainger • Manager, Search Technology Development @ CareerBuilder.comRelevant Background • Search & Recommendations • High-volume, N-tier Architectures • NLP, Relevancy Tuning, user group testing, & machine learningFun Side Projects • Founder and Chief Engineer @ .com • Currently co-authoring Solr in Action book… keep your eyes out for the early access release from Manning Publications
  4. 4. About Search @CareerBuilder• Over 1 million new jobs each month• Over 45 million actively searchable resumes• ~250 globally distributed search servers (in the U.S., Europe, & Asia)• Thousands of unique, dynamically generated indexes• Hundreds of millions of search documents• Over 1 million searches an hour
  5. 5. Search Products @
  6. 6. Redefining “Search Engine”• “Lucene is a high-performance, full-featured text search engine library…” Yes, but really…• Lucene is a high-performance, fully-featured token matching and scoring library… which can perform full-text searching.
  7. 7. Redefining “Search Engine” or, in machine learning speak:• A Lucene index is a multi-dimensional sparse matrix… with very fast and powerful lookup capabilities.• Think of each field as a matrix containing each term mapped to each document
  8. 8. The Lucene Inverted Index (traditional text example) How the content is INDEXED intoWhat you SEND to Lucene/Solr: Lucene/Solr (conceptually):Document Content Field Term Documentsdoc1 once upon a time, in a land a doc1 [2x] far, far away brown doc3 [1x] , doc5 [1x]doc2 the cow jumped over the cat doc4 [1x] moon. cow doc2 [1x] , doc5 [1x]doc3 the quick brown fox jumped over the lazy dog. … ...doc4 the cat in the hat once doc1 [1x], doc5 [1x]doc5 The brown cow said “moo” over doc2 [1x], doc3 [1x] once. the doc2 [2x], doc3 [2x],… … doc4[2x], doc5 [1x] … …
  9. 9. Match Text Queries to Text Fields /solr/select/?q=jobcontent: (software engineer)Job Content Field Documents engineer… … doc5engineer doc1, doc3, doc4, doc5 software engineer… doc1 doc3mechanical doc2, doc4, doc6 doc4… …software doc1, doc3, doc4, doc7, doc8 software… … doc7 doc8
  10. 10. Beyond Text Searching• Lucene/Solr is a text search matching engine• When Lucene/Solr search text, they are matching tokens in the query with tokens in index• Anything that can be searched upon can form the basis of matching and scoring: – text, attributes, locations, results of functions, user behavior, classifications, etc.
  11. 11. Business Case for Recommendations• For companies like CareerBuilder, recommendations can provide as much or even greater business value (i.e. views, sales, job applications) than user-driven search capabilities.• Recommendations create stickiness to pull users back to your company’s website, app, etc.• What are recommendations? … searches of relevant content for a user
  12. 12. Approaches to Recommendations• Content-based – Attribute based • i.e. income level, hobbies, location, experience – Hierarchical • i.e. “medical//nursing//oncology”, “animal//dog//terrier” – Textual Similarity • i.e. Solr’s MoreLikeThis Request Handler & Search Handler – Concept Based • i.e. Solr => “software engineer”, “java”, “search”, “open source”• Behavioral Based • Collaborative Filtering: “Users who liked that also liked this…”• Hybrid Approaches
  13. 13. Content-based Recommendation Approaches
  14. 14. Attribute-based Recommendations• Example: Match User Attributes to Item Attribute Fields Janes_Profile:{ Industry:”healthcare”, Locations:”Boston, MA”, JobTitle:”Nurse Educator”, Salary:{ min:40000, max:60000 }, } /solr/select/?q=(jobtitle:”nurse educator”^25 OR jobtitle:(nurse educator)^10) AND ((city:”Boston” AND state:”MA”)^15 OR state:”MA”) AND _val_:”map(salary,40000,60000,10,0)” //by mapping the importance of each attribute to weights based upon your business domain, you can easily find results which match your customer’s profile without the user having to initiate a search.
  15. 15. Hierarchical Recommendations• Example: Match User Attributes to Item Attribute Fields Janes_Profile:{ MostLikelyCategory:”healthcare//nursing//oncology”, 2ndMostLikelyCategory:”healthcare//nursing//transplant”, 3rdMostLikelyCategory:”educator//postsecondary//nursing”, … } /solr/select/?q=(category:( (”healthcare.nursing.oncology”^40 OR ”healthcare.nursing”^20 OR “healthcare”^10)) OR (”healthcare.nursing.transplant”^20 OR ”healthcare.nursing”^10 OR “healthcare”^5)) OR (”educator.postsecondary.nursing”^10 OR ”educator.postsecondary”^5 OR “educator”) ))
  16. 16. Textual Similarity-based Recommendations• Solr’s More Like This Request Handler / Search Handler are a good example of this.• Essentially, “important keywords” are extracted from one or more documents and turned into a search.• This results in secondary search results which demonstrate textual similarity to the original document(s)• See http://wiki.apache.org/solr/MoreLikeThis for example usage• Currently no distributed search support (but a patch is available)
  17. 17. Concept Based RecommendationsApproaches:1) Create a Taxonomy/Dictionary to define your concepts and then either: a) manually tag documents as they come in //Very hard to scale… see Amazon Mechanical Turk if you must do this or b) create a classification system which automatically tags content as it comes in (supervised machine learning) //See Apache Mahout2) Use an unsupervised machine learning algorithm to cluster documents and dynamically discover concepts (no dictionary required). //This is already built into Solr using Carrot2!
  18. 18. How Clustering Works
  19. 19. Setting Up Clustering in SolrConfig.xml<searchComponent name="clustering" enable=“true“class="solr.clustering.ClusteringComponent"> <lst name="engine"> <str name="name">default</str> <str name="carrot.algorithm"> org.carrot2.clustering.lingo.LingoClusteringAlgorithm</str> <str name="MultilingualClustering.defaultLanguage">ENGLISH</str> </lst></searchComponent><requestHandler name="/clustering" enable=“true" class="solr.SearchHandler"> <lst name="defaults"> <str name="clustering.engine">default</str> <bool name="clustering.results">true</bool> <str name="fl">*,score</str> </lst> <arr name="last-components"> <str>clustering</str> </arr></requestHandler>
  20. 20. Clustering Search in Solr• /solr/clustering/?q=content:nursing &rows=100 &carrot.title=titlefield &carrot.snippet=titlefield &LingoClusteringAlgorithm.desiredClusterCountBase=25 &group=false //clustering & grouping don’t currently play nicely• Allows you to dynamically identify “concepts” and their prevalence within a user’s top search results
  21. 21. Search: Nursing
  22. 22. Search: .Net
  23. 23. Example Concept-based Recommendation Stage 1: Identify Concepts Original Query: q=(solr or lucene) Clusters Identifier: Developer (22) // can be a user’s search, their job title, a list of skills, Java Developer (13) // or any other keyword rich data source Software (10) Senior Java Developer (9) Architect (6) Software Engineer (6) Web Developer (5) Search (3) Software Developer (3) Systems (3) Administrator (2)Facets Identified (occupation): Hadoop Engineer (2) Java J2EE (2)Computer Software Engineers Search Development (2)Web Developers Software Architect (2)... Solutions Architect (2)
  24. 24. Example Concept-based Recommendation Stage 2: Run Recommendations Searchq=content:(“Developer”^22 or “Java Developer”^13 or “Software”^10 or “Senior Java Developer”^9 or “Architect ”^6 or “SoftwareEngineer”^6 or “Web Developer ”^5 or “Search”^3 or “SoftwareDeveloper”^3 or “Systems”^3 or “Administrator”^2 or “HadoopEngineer”^2 or “Java J2EE”^2 or “Search Development”^2 or“Software Architect”^2 or “Solutions Architect”^2) andoccupation: (“Computer Software Engineers” or “WebDevelopers”)// Your can also add the user’s location or the original keywords to the// recommendations search if it helps results quality for your use-case.
  25. 25. Example Concept-based RecommendationStage 3: Returning the Recommendations …
  26. 26. Important Side-bar: Geography
  27. 27. Geography and Recommendations• Filtering or boosting results based upon geographical area or distance can help greatly for certain use cases: – Jobs/Resumes, Tickets/Concerts, Restaurants• For other use cases, location sensitivity is nearly worthless: – Books, Songs, Movies /solr/select/?q=(Standard Recommendation Query) AND _val_:”(recip(geodist(location, 40.7142, 74.0064),1,1,0))” // there are dozens of well-documented ways to search/filter/sort/boost // on geography in Solr.. This is just one example.
  28. 28. Behavior-based Recommendation Approaches (Collaborative Filtering)
  29. 29. The Lucene Inverted Index (user behavior example) How the content is INDEXED intoWhat you SEND to Lucene/Solr: Lucene/Solr (conceptually):Document “Users who bought this Term Documents product” Field user1 doc1, doc5doc1 user1, user4, user5 user2 doc2doc2 user2, user3 user3 doc2 user4 doc1, doc3,doc3 user4 doc4, doc5 user5 doc1, doc4doc4 user4, user5 … …doc5 user4, user1… …
  30. 30. Collaborative Filtering• Step 1: Find similar users who like the same documents q=documentid: (“doc1” OR “doc4”) Document “Users who bought this product “Field doc1 doc4 doc1 user1, user4, user5 user1 user4 user4 user5 doc2 user2, user3 user5 doc3 user4 doc4 user4, user5 Top Scoring Results (Most Similar Users): 1) user5 (2 shared likes) doc5 user4, user1 2) user4 (2 shared likes) … … 3) user 1 (1 shared like)
  31. 31. Collaborative Filtering• Step 2: Search for docs “liked” by those similar usersMost Similar Users:1) user5 (2 shared likes) /solr/select/?q=userlikes: (“user5”^22) user4 (2 shared likes) OR “user4”^2 OR “user1”^1)3) user 1 (1 shared like)Term Documents Top Recommended Documents:user1 doc1, doc5 1) doc1 (matches user4, user5, user1)user2 doc2 2) doc4 (matches user4, user5) 3) doc5 (matches user4, user1)user3 doc2 4) doc3 (matches user4)user4 doc1, doc3, doc4, doc5 //Doc 2 does not matchuser5 doc1, doc4 //above example ignores idf calculations… …
  32. 32. Lot’s of Variations• Users –> Item(s)• User –> Item(s) –> Users• Item –> Users –> Item(s)• etc. User 1 User 2 User 3 User 4 … Item 1 X X X … Item 2 X X … Item 3 X X … Item 4 X … … … … … … …Note: Just because this example tags with “users” doesn’t mean you have to.You can map any entity to any other related entity and achieve a similar result.
  33. 33. Comparison with Mahout• Recommendations are much easier for us to perform in Solr: – Data is already present and up-to-date – Doesn’t require writing significant code to make changes (just changing queries) – Recommendations are real-time as opposed to asynchronously processed off-line. – Allows easy utilization of any content and available functions to boost results• Our initial tests show our collaborative filtering approach in Solr significantly outperforms our Mahout tests in terms of results quality – Note: We believe that some portion of the quality issues we have with the Mahout implementation have to do with staleness of data due to the frequency with which our data is updated.• Our general take away: – We believe that Mahout might be able to return better matches than Solr with a lot of custom work, but it does not perform better for us out of the box.• Because we already scale… – Since we already have all of data indexed in Solr (tens to hundreds of millions of documents), there’s no need for us to rebuild a sparse matrix in Hadoop (your needs may be different).
  34. 34. Hybrid Recommendation Approaches
  35. 35. Hybrid Approaches• Not much to say here, I think you get the point.• /solr/select/?q=category:(”healthcare.nursing.oncology”^10 ”healthcare.nursing”^5 OR “healthcare”) OR title:”Nurse Educator”^15 AND _val_:”map(salary,40000,60000,10,0)”^5 AND _val_:”(recip(geodist(location, 40.7142, 74.0064),1,1,0))”)• Combining multiple approaches generally yields better overall results if done intelligently. Experimentation is key here.
  36. 36. Important Considerations &Advanced Capabilities @ CareerBuilder
  37. 37. Important Considerations @ CareerBuilder• Payload Scoring• Measuring Results Quality• Understanding our Users
  38. 38. Custom Scoring with Payloads• In addition to boosting search terms and fields, content within the same field can also be boosted differently using Payloads (requires a custom scoring implementation):• Content Field: design [1] / engineer [1] / really [ ] / great [ ] / job [ ] / ten[3] / years[3] / experience[3] / careerbuilder [2] / design *2+, … Payload Bucket Mappings: jobtitle: bucket=[1] boost=10; company: bucket=[2] boost=4; jobdescription: bucket=[] weight=1; experience: 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;2:4;3:1.5;default:1;• This allows us to map many relevancy buckets to search terms at index time and adjust the weighting at query time without having to search across hundreds of fields.• By making all scoring parameters overridable at query time, we are able to do A / B testing to consistently improve our relevancy model
  39. 39. Measuring Results Quality• A/B Testing is key to understanding our search results quality.• Users are randomly divided between equal groups• Each group experiences a different algorithm for the duration of the test• We can measure “performance” of the algorithm based upon changes in user behavior: – For us, more job applications = more relevant results – For other companies, that might translate into products purchased, additional friends requested, or non-search pages viewed• We use this to test both keyword search results and also recommendations quality
  40. 40. Understanding our Users(given limited information)
  41. 41. Understanding Our Users• Machine learning algorithms can help us understand what matters most to different groups of users. Example: Willingness to relocate for a job (miles per percentile) 2,500 2,000 Title Examiners, Abstractors, and Searchers 1,500 1,000 Software Developers, Systems Software 500 Food Preparation Workers 0 1% 5% 10% 20% 25% 30% 40% 50% 60% 70% 75% 80% 90% 95%
  42. 42. Key Takeaways• Recommendations can be as valuable or more than keyword search.• If your data fits in Solr then you have everything you need to build an industry-leading recommendation system• Even a single keyword can be enough to begin making meaningful recommendations. Build up intelligently from there.
  43. 43. Contact Info  Trey Grainger trey.grainger@careerbuilder.com http://www.careerbuilder.com @treygraingerAnd yes, we are hiring – come chat with me if you are interested.

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