michal barla: beyond search queries @ elasticsearch vienna meetup #1
DESCRIPTION
How do users interact with search engines? What can we learn from this behavior? How can we make search engines better? How do we measure quality of search results and what are the key metrics? Do you even measure the quality of your search? Let's take a walk standing on the shoulders of giants like Google, Yahoo or Yandex and learn about the recent advances in search research.TRANSCRIPT
Beyond search queriesMichal Barlasearchd.co
About me
● researcher and teacher at Slovak University of Technology in Bratislava
● developer @ synopsi.tv, searchd.co● co-owner of minio, s.r.o.
○ otvorenezmluvy.sk, govdata.sk
Searchas seen by developers
{ "query": { "query_string": { "query": "elasticsearch book" } }}
return response.hits.hits
Searchas experienced by users
query: elasticsarchTypo in query. No results.
query: elasticsearch Too many hits. Not relevant.
query: elasticsearch bookClick!Success! Or?
Measuring search quality
Cpt. Obvious: “Hits, clicks and order
do matter.”
Accurately interpreting clickthrough data as implicit feedback
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in Information retrieval, SIGIR ’05, pages 154–161, New York, NY, USA, 2005. ACM.
Accurately interpreting clickthrough data as implicit feedback
Search quality metrics
● Mean Average Precision @ N○ probability of target result being in top N items
● Mean Reciprocal Rank○ 1 / rank of target result
● Normalized Discounted Cumulative Gain● Expected Reciprocal Rank
Search KPIs
● CTR trend
● # of queries w/o results or clicks
● # of searches per session
● Search engine latency
Search quality optimization
Optimizing search engines using clickthrough data
Thorsten Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’02, pages 133–142, New York, NY, USA, 2002. ACM.
Optimizing search engines using clickthrough data
Query chains: learning to rank from implicit feedback
Filip Radlinski and Thorsten Joachims. Query chains: learning to rank from implicit feedback. In KDD ’05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 239–248, New York, NY, USA, 2005. ACM.
Fighting Search Engine Amnesia: Reranking Repeated Results
Milad Shokouhi, Ryen W. White, Paul Bennett, and Filip Radlinski. Fighting search engine amnesia: reranking repeated results. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’13, pages 273–282, New York, NY, USA, 2013. ACM.
In this paper, we observed that the same results are often shown to users multiple times during search sessions. We showed that there are a number of effects at play, which can be leveraged to improve information retrieval performance. In particular, previously skipped results are much less likely to be clicked, and previously clicked results may or may not be re-clicked depending on other factors of the session.
searchd.coSearch Analytics
searchd.co dashboard
searchd.coSearch Analytics
● Identify and fix key search problems ● KPIs for site search● Actionable tips for search tuning
● Easy setupa. Add our hosted JavaScriptb. Annotate search results with HTML5 tagsc. Done.
● Currently in private beta
Bad search experience is a lost opportunity. Let's fix it.
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