HOW BUILDING OUR OWN E-COMMERCE SEARCH CHANGED OUR APPROACH TO SEARCH QUALITY. 2018// Berlin
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1 HOW BUILDING OUR OWN E-COMMERCE SEARCH CHANGED OUR APPROACH TO SEARCH QUALITY
2 Our Search Search Team in 2017 Christine Bellstedt Business Designer Search Search Quality Management Jens Kürsten Technical Designer Search Search Developer Team Web Analytics Business Intelligence
3 About OTTO Facts and figures about OTTO* Founded August 17th, 1949 No. of employees Revenue in 2017/18 FY billion Euros No. of orders on otto.de up to 10 per second Visits on otto.de per day on average 1.9 million *For more details: 3
4 Key Facts About in 2017 Ø search queries per day max. search queries per day search queries in unique search queries in
5 Key Requirement for Search USER INTENT & BUSINESS GOALS Search isdetermined by our user queries product data (quality) different performance indicators of our products different business goals for different categories Ø Bringing together the user s and the business perspective is our key requirement for search
6 What Is the Business Impact of Our Queries? 50% query volume covered by roughly 3000 unique queries Howmany unique queries are needed to cover 80% of query volume? 80% query volume covered by unique queries
7 What Is the Business Impact of Our Queries? 50% query volume covered by roughly 3500 unique queries Testing our new search solution on-line only might be expensive! 80% query volume covered by unique queries
8 What Are the Implications on Search Quality Testing? know your top queries and get them right early (short head covered) and keep them tested (automation ftw!) find a strategy to deal with the heterogeneity of long tail queries and create hypotheses and test them and keep repeating à implement a search quality testing cycle * * Peter Fries talk at Haystack Conference: Search Quality 8 - A Business-Friendly Perspective
9 Building Blocks of Our Experiment Framework Query Logs Click Logs Online Offline Legacy Search Ranking Feature Space New Search??? Ranking Configuration
10 Building Blocks: Ranking Features Query Logs Click Logs Online Offline Legacy Search MAIN RANKING Ranking Feature Space Relevance Score ( topicality ) New Search What kind of product isouruser searching for? Prediction of performance indicators How well will a product perform in relation to defined performance indicators (e.g. profit)? Availability of products How fast can we deliver a product?
11 Experiment Framework: Overview Query Logs Click Logs Online Legacy Search Offline Ranking Feature Space Test Query Set Global Optimization: Grid Search Judgements Linear New Combination Search of Parameter MetricsWeights: Topicality à Solr Document Score (+ DisMax.Tie) Ranking Configuration Quality Assessment Business Performance Indicators à Import Fields Availability à JudgmentDiscounts
12 What Are the Tricky Parts for Signifcant Results? Challenges for Test Query Selection Query Set Size (Coverage vs. Experiment Runtime) Query Sampling Bias (Long Tail, 0-Hits, Term Permutations, ) Temporal Bias (Seasonal Effects, Promotions, ) Challenges for Judgement Creation Selection of Success Criteria à Click Through: Detail View Click Data Coverage à Minimum Cut-Off Click Position Bias à Oversampling (Clicks * Rank^1/2) Query Frequency Bias à Normalize by Max & Minimum Cut-Off User Sampling Bias à L
13 What Did We Learn Along theway? Ranking Features Selection Is Hard, Priorisation Is Even Harder Interactions Will Appear Dimensionalty of Feature Values Is Enormous Experiment Execution Check the Test Data ScalabiltyofExperiment Cluster Check Your Query Execution Logs Quality Assessment Metric Selection Is Hard Success Criteria & Business Impact Take a Close Look at Statistical Precision/Variance
14 Today & Future Perspectives Online Offline Ranking Feature Space Query Logs Test Query Set Champion vs. Challenger Global Optimization via Grid Search Candidate(s) Click Logs Judgements Metrics Success Criteria Evaluation & Combination Offline Setup: Further Automation Ranking Configuration Quality Assessment Global Features Local Features Product Contexts Query Features User Prefs LocalOptimization via Learning to Rank Models
15 Team Perspective: Keyword Engineering à Feature Engineering BEFORE AFTER Search Developer Team Search Quality Management Web Analytics Software Developers Product Managers Data Scientists & Engineers IR Specialists Business Intelligence
16 Connect We are hiring
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