Enhancing relevancy through personalization & semantic search
Upcoming SlideShare
Loading in...5
×
 

Enhancing relevancy through personalization & semantic search

on

  • 2,249 views

 

Statistics

Views

Total Views
2,249
Views on SlideShare
1,758
Embed Views
491

Actions

Likes
6
Downloads
60
Comments
0

5 Embeds 491

http://blog.livedoor.jp 274
http://www.lucenerevolution.org 207
http://lucenerevolution.org 5
http://rss.ameba.jp 4
http://feedly.com 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Enhancing relevancy through personalization & semantic search Enhancing relevancy through personalization & semantic search Presentation Transcript

  • Dublin, IE 2013.11.07 Trey Grainger ENHANCING RELEVANCY THROUGH PERSONALIZATION & SEMANTIC SEARCH Search Technology Development Manager @"
  • My Background Trey"Grainger" Search"Technology"Development"Manager" ""@CareerBuilder.com" " Relevant"Background" •  Search"&"Recommenda>ons" •  HighAvolume,"Distributed"Systems" •  NLP,"Relevancy"Tuning,"User"Group"Tes>ng,"&"Machine"Learning" " """""""""""""""""""""""""""Other"Projects" •  CoAauthor:""Solr%in%Ac*on% •  Founder"and"Chief"Engineer"@"""""""""""""""""""""""""".com"
  • Roadmap •  •  •  I. How we use Solr @ CareerBuilder II. Traditional Relevancy Scoring III. Advanced Relevancy through functions –  Factors as a linear function –  Context-aware relevancy parameter weighting •  III. Personalization & Recommendations –  Profile and Behavior-based –  Solr as a recommendation engine –  Collaborative Filtering •  IV. Semantic Search –  –  –  –  –  Mining user-behavior for synonyms Uncovering meaning through clustering Latent Semantic Indexing overview Document-based searching Foreground vs. Background analysis
  • How"we"use"Solr"@"CareerBuilder"
  • Search Scale @ •  •  •  •  •  •  Over"2.5"million"new"jobs"each"month"" Over"60"million"ac>vely"searchable"resumes" ~300"globally"distributed"search"servers"" Thousands"of"unique,"dynamically"generated"indexes" Over"1"Billion"ac>vely"searchable"documents" Over"1"million"searches"an"hour"
  • Data Analytics
  • Data Analytics
  • Data Analytics (market supply)
  • Data Analytics (market demand)
  • Data Analytics (labor pressure: supply/demand)
  • Data Analytics (hiring comparison per market)
  • Traditional Search
  • Recommendations
  • Tradi>onal"Relevancy"Scoring"
  • Default Lucene Relevancy Algorithm (DefaultSimilarity) Score(q,d)"=""" """"""∑""("-(t"in"d)".""idf(t)2"."t.getBoost()"."norm(t,"d)")6.6coord(q,"d)".6queryNorm(q) """""t"in"q" """" " Where:"" "t"="term;"d"="document;"q"="query;"f"="field" 666666666-(t"in"d)""=""numTermOccurrencesInDocument"½" 666666666idf(t)"=""1"+"log"(numDocs"/"(docFreq"+"1))" 666666666coord(q,"d)"="numTermsInDocumentFromQuery"/"numTermsInQuery" 666666666queryNorm(q)"="1"/"(sumOfSquaredWeights"½")" 666666666sumOfSquaredWeights"="q.getBoost()2"."∑"("idf(t)"."t.getBoost()")2"" """""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""t"in"q" 666666666norm(t,"d)"""="""d.getBoost()""f""lengthNorm(f)""f"""f.getBoost()" *Source:"Solr%in%Ac*on,"chapter"3" 6
  • TF * IDF •  Term Frequency: “How well a term describes a document?” –  Measure: how often a term occurs per document •  Inverse Document Frequency: “How important is a term overall?” –  Measure: how rare the term is across all documents
  • Boosting documents and fields •  Certain fields may be more important than other fields: –  The Job Title and Skills may be more relevant than other aspects of the job: /select?qf=jobtitle^10 skills^5 jobrequirements^2 jobdescription^1 •  It’s possible to boost documents and fields at both index time and query time •  If you need more fine-grained control (such as per-term index-time boosting), you can make use of payloads
  • Custom scoring with Payloads •  In addition to boosting search terms and fields, content within Fields can also be boosted differently using Payloads (requires a custom scoring implementation): design [1] / engineer [1] / really [ ] / great [ ] / job [ ] / ten[3] / years[3] / experience[3] / careerbuilder [2] / design [2], … 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
  • That’s great, but what about domain-specific knowledge? •  •  •  •  •  News search: popularity and freshness drive relevance Restaurant search: geographical proximity and price range are critical Ecommerce: likelihood of a purchase is key Movie search: More popular titles are generally more relevant Job search: category of job, salary range, and geographical proximity matter TF * IDF of keywords can’t hold it’s own against good domain-specific relevance factors!
  • Advanced"Relevancy"through"Func>ons"
  • Example of domain-specific relevancy calculation News website: /select? fq=$myQuery& 25%" q=_query_:"{!func}scale(query($myQuery),0,100)" AND _query_:"{!func}div(100,map(geodist(),0,1,1))" 25%" AND _query_:"{!func}recip(rord(publicationDate),0,100,100)" 25%" AND _query_:"{!func}scale(popularity,0,100)"& myQuery="street festival"& 25%" sfield=location& pt=33.748,-84.391 *Example"from"chapter"16"of"Solr%in%Ac*on%
  • Fancy boosting functions •  Separating “relevancy” and “filtering” from the query: q=_val_:"$keywords"&fq={!cache=false v=$keywords}&keywords=solr •  Keywords (50%) + distance (25%) + category (25%) q=_val_:"scale(mul(query($keywords),1),0,50)" AND _val_:"scale(sum($radiusInKm,mul(query($distance),-1)),0,25)” AND _val_:"scale(mul(query($category),1),0,25)" &keywords=solr &radiusInKm=48.28 &distance=_val_:"geodist(latitudelongitude.latlon_is,33.77402,-84.29659)” &category=jobtitle:"java developer" &fq={!cache=false v=$keywords}
  • Context aware relevancy Example: Willingness to relocate for a job 2,500" 2,000" 1,500" 1,000" 500" 0" So>ware6engineers6 Food6service6workers6 1%" 5%" 10%" 20%" 25%" 30%" 40%" 50%" 60%" 70%" 75%" 80%" 90%" 95%"
  • Willingness to relocate Somware"engineers"in"Chicago"want"jobs"in"these"loca>ons:"
  • Willingness to relocate Food"service"workers"in"Chicago"want"jobs"in"these"loca>ons:"
  • Personaliza>on"&"Recommenda>ons"
  • Beyond domain knowledge… consider per-user knowledge •  John lives in Boston but wants to move to New York or possibly another big city. He is currently a sales manager but wants to move towards business development. •  Irene is a bartender in Dublin and is only interested in jobs within 10KM of her location in the food service industry. •  Irfan is a software engineer in Atlanta and is interested in software engineering jobs at a Big Data company. He is happy to move across the U.S. for the right job. •  Jane is a nurse educator in Boston seeking between $40K and $60K working in the healthcare industry
  • Query for Jane Jane is a nurse educator in Boston seeking between $40K and $60K working in the healthcare industry http://localhost:8983/solr/jobs/select/? fl=jobtitle,city,state,salary& 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)” *Example from chapter 16 of Solr in Action
  • Search Results for Jane { ... "response":{"numFound":22,"start":0,"docs":[ {"jobtitle":"Clinical Educator (New England/ Boston)", "city":"Boston", "state":"MA", "salary":41503}, {"jobtitle":"Nurse Educator", "city":"Braintree", "state":"MA", "salary":56183}, {"jobtitle":"Nurse Educator", "city":"Brighton", "state":"MA", "salary":71359} …]}} *Example documents available @ http://github.com/treygrainger/solr-in-action/ "
  • What did we just do? •  We built a recommendation engine! •  What is a recommendation engine? –  A system that uses known information (or derived information from that known information) to automatically suggest relevant content •  Our example was just an attribute based recommendation… we’ll see that behavioral-based (i.e. collaborative filtering) is also possible.
  • Redefining “Search Engine” •  “Lucene is a high-performance, full-featured text search engine library…” Yes,6but6really…6 •  "Lucene"is"a"highAperformance,"fullyAfeatured" token"matching"and"scoring"library…"which" can"perform"fullAtext"searching."
  • Redefining “Search Engine” or,6in6machine6learning6speak:6 •  A"Lucene"index"is"mul>Adimensional"" sparse"matrix…"with"very"fast"and"powerful"lookup" capabili>es." •  Think"of"each"field"as"a"matrix"containing"each"term" mapped"to"each"document"
  • The Lucene Inverted Index (traditional text example) What6you6SEND6to6Lucene/Solr:6 How6the6content6is6INDEXED6into6 Lucene/Solr6(conceptually):6 Document6 Content6Field6 Term6 Documents6 doc1"" once"upon"a">me,"in"a"land"far,"far" away" a" doc1"[2x]" brown" doc2" the"cow"jumped"over"the"moon." doc3"[1x]","doc5"[1x]" cat" doc4"[1x]" doc3"" the"quick"brown"fox"jumped"over" the"lazy"dog." cow" doc2"[1x]","doc5"[1x]" …" ...6 doc4" the"cat"in"the"hat" once" doc1"[1x],"doc5"[1x]" doc5" The"brown"cow"said"“moo”"once." over" doc2"[1x],"doc3"[1x]" the" …" …" doc2"[2x],"doc3"[2x]," doc4[2x],"doc5"[1x]" …" …"
  • Matching text queries to text fields /solr/select/?q=jobcontent:“software engineer” Job6Content6Field6 Documents6 …" …" engineer" doc1,"doc3,"doc4,"doc5" engineer" doc5" somware"engineer" …" mechanical" doc2,"doc4,"doc6" …" …6 somware" doc1,"doc3,"doc4,"doc7," doc8" …" …" doc1"""""doc3"""" """""""doc4" somware" doc7"""""doc8"
  • Beyond Text Searching •  Lucene/Solr"is"a"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,"atributes,"loca>ons,"results"of"func>ons,"user" behavior,"classifica>ons,"etc.""
  • 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” •  Collaborative Filtering “Users who liked that also liked this…” •  Hybrid Approaches
  • Collaborative Filtering What6you6SEND6to6Lucene/Solr:6 Document6 “Users6who6bought6this6product”6field6 doc1"" How6the6content6is6INDEXED6into6 Lucene/Solr6(conceptually):6 Term6 Documents6 user1,"user4,"user5" user1" doc1,"doc5" doc2" user2,"user3" user2" doc2" doc3"" user4" user3" doc2" doc4" user4,"user5" user4" doc5" user4,"user1" doc1,"doc3,"" doc4,"doc5" …" …" user5" doc1,"doc46 …" …"
  • Step 1: Find similar users who like the same documents q=documen>d:"("doc1""OR""doc4")" Document6 “Users6who6bought6this6product”6field6 doc1"" user1,"user4,"user5" doc2" user2,"user3" doc3"" user4" doc4" user4,"user5" doc5" user4,"user1" …" …" *Source:"Solr%in%Ac*on,"chapter"16" doc16 user166666user466 6666 666666666user56 doc46 666user466666user56 TopAscoring"results"(most"similar"users):" 1)  "user4"(2"shared"likes)" 2)  "user5"(2"shared"likes)" 3)  "user"1"(1"shared"like)"
  • " Step 2: Search for docs “liked” by those similar users """ Most"similar"users:" 1)  "user4"(2"shared"likes)" """""""""""""""""""""""""""""""""""""""""""""""""""""""/solr/select/?q=userlikes:("user4"^2"" " " 2)  "user5"(2"shared"likes)" " """""""""""""""""""""""""""""""""""""""""""""""""""""""""OR""user5"^2"OR""user1"^1)" 3)  "user"1"(1"shared"like)" Term6 Documents6 user1" doc1,"doc5" user2" doc2" user3" doc2" user4" doc1,"doc3,"" doc4,"doc5" user5" doc1,"doc46 …" …" *Source:"Solr%in%Ac*on,"chapter"16" Top"recommended"documents:" 1)"doc1"(matches"user4,"user5,"user1)" 2)"doc4"(matches"user4,"user5)" 3)"doc5"(matches"user4,"user1)" 4)"doc3"(matches"user4)" " //"doc2"does"not"match"
  • Building up to personalization •  Use what you have: –  User’s keywords, IP address, searches, clicks, “likes” (purchases, job applications, comments, etc.) –  Build up a dossier of information on your users –  If a user gives you a profile (resume, social profile, etc), even better.
  • For full coverage of building a recommendation engine in Solr… •  See my talk from Lucene Revolution 2012 (Boston):
  • Personalized Search •  Why limit yourself to JUST explicit search or JUST automated recommendations? •  By augmenting your user’s explicit queries with information you know about them, you can personalize their search results. •  Examples: –  A known software engineer runs a blank job search in New York… •  Why not show software engineering higher in the results? –  A new user runs a keyword-only search for nurse •  Why not use the user’s IP address to boost documents geographically closer?
  • Seman>c"Search"
  • Not going to talk about… •  Using the SynonymFilter •  Automatic language detection •  Stemming/lemmatization/multi-lingual search •  Stopwords (For all of the above, see the Solr Wiki, Reference Guide, or read Solr in Action) •  Instead, we’re going to cover: –  Mining user behavior to discover synonyms/related queries –  Discovering related concepts using document clustering in Solr –  Future work: Latent Semantic Indexing –  Document to Document searching using More Like This –  Foreground/Background corpus analysis
  • Automatic Synonym Discovery •  •  Our primary approach: Search Co-occurrences Strategy: Map/Reduce job which computes similar searches run for the same users John searched for “java developer” and “j2ee” Jane searched for “registered nurse” and “r.n.” and “prn”. Zeke searched for “java developer” and “scala” and “jvm” •  By mining the searches of tens millions of search terms per day, we get a list of top searches, with the corresponding top co-occurring searches. •  We also tie each search term to the top category of jobs (i.e java developer, truck driver, etc.), so that we know in what context people search for each term.
  • Example of “related search terms” Example:"“RN”:" registered"nurse"6588," rn"registered"nurse"4300," nurse"2492," nursing"912," lpn"707," healthcare"453," rn"case"manager"446," registered"nurse"rn"404," director"of"nursing"321," case"manager"292" Example:"“accoun>ng”" accountant"8880," accounts"payable"5235," finance"3675," accoun>ng"clerk"3651," bookkeeper"3225," controller"2898," staff"accountant"2866," accounts"receivable"2842"
  • Future work on building conceptual links Latent Semantic Indexing •  Concept: Build a matrix of all terms, perform singular value decomposition on that Matrix to reduce the number of dimensions, and index the meaningful (i.e. blurred) terms on each document. •  Why this matters: if done correctly, the search engine can automatically collapse terms by meaning, remove the useless and redundant ones, and for it’s own conceptual model of your domain space. This can be used to infuse more meaning into a document than just a keyword. •  See blog posts and presentations by John Berryman and Doug Turnbull about their work on this. They’re leading the way on this right now (in the open-source community). •  http://www.opensourceconnections.com/2013/08/25/semantic-search-with-solr-and-python-numpy
  • Using Clustering to find semantic links
  • 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="lastIcomponents">" ....<str>clustering</str>" ..</arr>" </requestHandler>"
  • Clustering Query /solr/clustering/?q=(solr or lucene) &rows=100 &carrot.title=titlefield &carrot.snippet=titlefield &LingoClusteringAlgorithm.desiredClusterCountBase=25 //clustering & grouping don’t currently play nicely Allows you to dynamically identify “concepts” and their prevalence within a user’s top search results
  • Clustering Results Stage"1:"Iden>fy"Concepts" Original"Query:"""q=(solr"or"lucene)"""" " " " " " " "//"can"be"a"user’s"search,"their"job">tle,""a"list"of"skills," //"or"any"other"keyword"rich"data"source" Clusters Identified: " Developer (22) Java Developer (13) Software (10) Senior Java Developer (9) Architect (6) Software Engineer (6) Web Developer (5) Search (3) " " """""""""""""""""""" Software Developer (3) Systems (3) Administrator (2) Hadoop Engineer (2) Java J2EE (2) Search Development (2) Software Architect (2) Solutions Architect (2)
  • Stage"2:"Use"Seman>c"Links"in"your"relevancy"calcula>on" q=content:(“Developer”^22"or"“Java"Developer”^13"or"“Somware" ”^10"or"“Senior"Java"Developer”^9""or"“Architect"”^6"or"“Somware" Engineer”^6"or"“Web"Developer"”^5"or"“Search”^3"or"“Somware" Developer”^3"or"“Systems”^3"or"“Administrator”^2"or"“Hadoop" Engineer”^2"or"“Java"J2EE”^2"or"“Search"Development”^2"or" “Somware"Architect”^2"or"“Solu>ons"Architect”^2)6 6 //6Your6can6also6add6the6user’s6loca[on6or6the6original6keywords6to6the66 //6recommenda[ons6search6if6it6helps6results6quality6for6your6usecase."
  • Document to Document Searching Goal: use an entire document as your Solr Query, recommending other related documents. Standard approach: More Like This Handler Alternative Approach: Foreground vs. Background corpus analysis
  • More Like This (Query) solrconfig.xml: <requestHandler name="/mlt" class="solr.MoreLikeThisHandler" /> Query: /solr/jobs/mlt/?df=jobdescription& fl=id,jobtitle& rows=3& q=J2EE& // recommendations based on top scoring doc mlt.fl=jobtitle,jobdescription& // inspect these fields for interesting terms mlt.interestingTerms=details& // return the interesting terms mlt.boost=true *Example"from"chapter"16"of"Solr%in%Ac*on%
  • More Like This (Results) {"match":{"numFound":122,"start":0,"docs":[ {"id":"fc57931d42a7ccce3552c04f3db40af8dabc99dc", "jobtitle":"Senior Java / J2EE Developer"}] }, "response":{"numFound":2225,"start":0,"docs":[ {"id":"0e953179408d710679e5ddbd15ab0dfae52ffa6c", "jobtitle":"Sr Core Java Developer"}, {"id":"5ce796c758ee30ed1b3da1fc52b0595c023de2db", "jobtitle":"Applications Developer"}, {"id":"1e46dd6be1750fc50c18578b7791ad2378b90bdd", "jobtitle":"Java Architect/ Lead Java Developer WJAV Java - Java in Pittsburgh PA"},]}, ""interes>ngTerms":[" " " """""""" """"""jobdescrip>on:j2ee",1.0," """"""jobdescrip>on:java",0.68131137," """"""jobdescrip>on:senior",0.52161527," """"""job>tle:developer",0.44706684," """"""jobdescrip>on:source",0.2417754," """"""jobdescrip>on:code",0.17976432," """"""jobdescrip>on:is",0.17765637," """"""jobdescrip>on:client",0.17331646," """"""jobdescrip>on:our",0.11985878," """"""jobdescrip>on:for",0.07928475," """"""jobdescrip>on:a",0.07875194," """"""jobdescrip>on:to",0.07741922," """"""jobdescrip>on:and",0.07479082]}}"
  • More Like This (passing in external document) /solr/jobs/mlt/? df=jobdescription& fl=id,jobtitle& mlt.fl=jobtitle,jobdescription& mlt.interestingTerms=details& mlt.boost=true stream.body=Solr is an open source enterprise search platform from the Apache Lucene project. Its major features include full-text search, hit highlighting, faceted search, dynamic clustering, database integration, and rich document (e.g., Word, PDF) handling. Providing distributed search and index replication, Solr is highly scalable. Solr is the most popular enterprise search engine. Solr 4 adds NoSQL features.
  • More Like This (Results) {"response":{"numFound":2221,"start":0,"docs":[ {"id":"eff5ac098d056a7ea6b1306986c3ae511f2d0d89 ", •  "jobtitle":"Enterprise Search Architect…"}, {"id":"37abb52b6fe63d601e5457641d2cf5ae83fdc799 ", "jobtitle":"Sr. Java Developer"}, {"id":"349091293478dfd3319472e920cf65657276bda4 ", "jobtitle":"Java Lucene Software Engineer"},]}, ""interes>ngTerms":[" """"""jobdescrip>on:search",1.0," """"""jobdescrip>on:solr",0.9155779," """"""jobdescrip>on:features",0.36472517," """"""jobdescrip>on:enterprise",0.30173126," """"""jobdescrip>on:is",0.17626463," """"""jobdescrip>on:the",0.102924034," """"""jobdescrip>on:and",0.098939896]}"}"
  • CareerBuilder’s Alternative approach (“enhanced” More Like This) I. Send document as content stream to Solr II. Perform Language Identification on the content III. Do language-specific parts of speech detection •  Keep nouns, remove other parts of speech (removes noise) IV. Do analysis of additional terms for statistical significance: tf * idf OR foreground vs. background corpus comparison OR Both Preferred statistical significance measure: countFG(x) - totalCountFG * probBG(x) z= -------------------------------------------------------sqrt(totalCountFG * probBG(x) * (1 - probBG(x))) V. Return top scoring terms
  • Foreground vs. Background Corpus Comparison /solr/doc2doc? fg=category:"software engineer"&bg=*:*&stream.body=java nurse and is are was were ruby php solr oncology part-time … other text in a really long document” Terms statistically more likely to appear in foreground query than background query: java ruby We"are"essen>ally"boos>ng"terms"which"are"more"related"to" some"known"feature"(and"ignoring"terms"which"are"equally" php likely"to"appear"in"the"background"corpus)" document Note: This method requires you pre-classify your documents (which we do)… it doesn’t work with a document that hasn’t already been classified.
  • Pulling it all together Tradi>onal" Search" Personalized" Search" Profit!" Seman>c" Search" Recommenda>ons"
  • Take-aways •  Lucene’s inverted index is a sparse matrix useful for traditional search (keywords, locations, etc.), recommendations, and discovering links between terms/tokens •  Traditional tf * idf keyword search is a good starting point, but the best relevancy lies in combining your domain knowledge (knowledge of user’s in aggregate) and user-specific knowledge into your own relevancy factors. •  The ability to understand user queries (semantic search) further enhances the search experience, and you already have many tools at your fingertips for this.
  • Questions? !  Trey6Grainger6 trey.grainger@careerbuilder.com6 @treygrainger6 6 6 6 6 Other6presenta[ons:666 66666h_p://www.treygrainger.com6 htp://solrinac>on.com"