Learning-to-Rank with Apache Solr & Bees - Christine Poerschke, Bloomberg
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Learning-to-Rank with Apache Solr & Bees - Christine Poerschke, Bloomberg
- 1. Learning-to-Rank with Apache Solr and Bees Christine Poerschke Bloomberg
- 2. 2 01 Hello • BSc, PhD • Software Developer in the News Search Infrastructure team at Bloomberg • Apache Lucene/Solr committer and PMC member • Apache Software Foundation member • spare-time beekeeper
- 4. Agenda • Get started with Solr's Learning-to-Rank plugin • Demystify machine learning steps involved • Hands-on, take-home materials: https://github.com/cpoerschke/ltr-with-bees
- 5. 5 01 Tools • Apache Solr 6.6.1 • Twitter REST APIs • LIBLINEAR 2.11 • RankLib 2.8 • https://github.com/cpoerschke/ltr-with-bees
- 6. 6 01 Happy Users? • Bianca is a beekeeper and software engineer. She is fascinated by honeybees, bumblebees, solitary bees and bee science. • Harry is Bianca's friend and works as a pastry chef. Harry loves honey.
- 7. 7 01 Too many search results? https://twitter.com/DaveGoulson/status/334232443563433985 https://twitter.com/1PortionDeutsch/status/827139009277227008
- 8. 8 01 Learning-to-Rank (LTR) "Learning to rank or machine-learned ranking (MLR) is the application of machine learning ... in the construction of ranking models for information retrieval systems. …" https://en.wikipedia.org/wiki/Learning_to_rank An illustration: 1. top 10 documents wanted 2. fetch 100 scored documents 3. calculate 100 revised scores 4. re-sort the 100 documents based on the revised scores 5. return the top 10 documents
- 9. 9 01 Feed Me Data ltr-with-bees.py solr-search- and-log-features tweet:honey ltr-with-bees.py solr-log-click $QUERY_ID $RESULT_ID
- 10. 10 01 Features org.apache.solr.ltr.feature • FieldLengthFeature • FieldValueFeature • OriginalScoreFeature • SolrFeature • ValueFeature
- 11. 11 01 SolrFeature: honeyContent { "name" : "honeyContent", "class" : "SolrFeature", "params" : { "q" : “tweet:honey" } } https://twitter.com/BeeCulture/status/777947618311860224
- 12. 12 01 FieldLengthFeature: hashtagCount { "name" : "hashtagCount", "class" : "FieldLengthFeature", "params" : { "field" : "hashtag" } } https://twitter.com/BeeCulture/status/777947618311860224
- 13. 13 01 FieldValueFeature: byVerifiedAccount { "name" : "byVerifiedAccount", "class" : "FieldValueFeature", "params" : { "field" : "verified_account" } } https://twitter.com/MomDot/status/761328145995751425
- 14. 14 01 ValueFeature: fromMobile { "name" : "fromMobile", "class" : "ValueFeature", "params" : { "value" : "${from_mobile:0}" } } https://twitter.com/NYTScience/status/784468395420946432
- 15. 15 01 SolrFeature: logFollowersCountForRetweet https://twitter.com/TAMUScience/status/568450251612233728 { "name":"logFollowersCountForRetweet", "class" : "SolrFeature", "params" : { "q" : "{!func}log(followers_count)", "fq" : [ "retweet_source:*" ] } }
- 16. 16 01 Feature Extraction: fl=source,tweet,[fv] "source" : "https://twitter.com/Joannechocolat/status/ 809365630319259652", "tweet" : "2. To the ancient Egyptians, bees were born from the tears of the Sun god, Ra. #TenThingsAboutBees", "[fv]" : "byVerifiedAccount:1.0 containsHashtag:1.0 containsHoney:0.0 containsMention:0.0 followersCount:52331.0 followingCount:959.0 fromDesktop:0.0 fromMobile:0.0 hashtagCount:1.0 honeyContent:0.0 logFollowersCount:4.718759 logFollowersCountForRetweet:0.0 logFollowingCount:2.9818187 logFollowingCountForRetweet:0.0 mentionCount:0.0 originalScore:0.0 tweetLength:20.897959"
- 17. 17 01 Click Signals # query_id result_id time_of_click ... ... ........................ 1 101 2017-08-31T00:00:00.000Z 3 ... ........................ 1 102 2017-08-31T00:01:00.000Z 5 ... ........................ 1 101 2017-09-15T............. ... ... ........................
- 19. 19 01 Features + Clicks = Linear Model ltr-with-bees.py train-linear-model —feature- names "hashtagCount,fromMobile,honeyContent" # cat inputs.txt -1 8:0.0 9:0.0 10:0.0 +1 8:0.0 9:1.0 10:0.0 +1 8:1.0 9:7.111111 10:1.4988465 ... liblinear-2.11/train inputs.txt outputs.txt
- 20. 20 01 org.apache.solr.ltr.model.LinearModel { "class" : "org.apache.solr.ltr.model.LinearModel", "name" : "myLinearModel", "features" : [ { "name" : "hashtagCount" }, { "name" : "fromMobile" }, { "name" : "honeyContent" } ], "params" : { "weights" : { "hashtagCount" : 0.05480027559114577, "fromMobile" : -0.01278328352290356, "honeyContent" : -0.1238891413021157 } } }
- 22. 22 01 Features + Clicks != Trees Model ltr-with-bees.py train-trees-model --feature-names "byVerifiedAccount,containsHashtag, fromDesktop,fromMobile,hashtagCount ,tweetLength" --ranklib-tree=1 --ranklib-leaf=4
- 23. 23 01 Features + Clicks + ??? = Trees Model java -jar RankLib-2.8.jar ... -ranker <type> Specify which ranking algorithm to use 0: MART (gradient boosted regression tree) ... 6: LambdaMART ... {MART, LambdaMART}-specific parameters [ -tree <t> ] Number of trees (default=1000) [ -leaf <l> ] Number of leaves for each tree (default=10) ...
- 24. 24 01 o.a.solr.ltr.model.MultipleAdditiveTreesModel "params" : { "trees" : [ { "weight" : "0.1", "root" : { "feature" : "containsHashtag", "threshold" : "0.0", "left" : { "feature" : "fromDesktop", "threshold" : "0.0", "left" : { "value" : "1.25" }, "right" : { "feature" : "byVerifiedAccount", "threshold" : "0.0", "left" : { "value" : "1.66666662693" }, "right" : { "value" : "1.0" } } }, "right" : { "value" : "1.75" } } } ] }
- 26. 26 01 select?q="key points"&rq={!ltr model=wrap} • Learning-to-Rank with Apache Solr is a journey. • You now have a sense of the adventures ahead. • Please don't send postcards. Please do email solr-user@, file JIRA tickets, talk and/or blog about your use case.
- 27. Thank You