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Rank 
all the things! 
@jsuchal 
@SynopsiTV
Blogs, newsletters 
Courses, training 
How do you learn things? 
Conferences Work
Research papers?
WHY NOT?
“It’s not useful for the 
real-world.” 
WHY NOT? 
“I wouldn’t 
understand any of 
that.”
About me 
PhD dropout FIIT STU Bratislava 
foaf.sk, otvorenezmluvy.sk, govdata.sk 
sme.sk news recommender 
developer @ Sy...
My workflow
My workflow 
MAGIC! 
MAGIC! 
MAGIC!
Search vs. recommender engine 
Search engine 
input: query 
output: list of results 
Recommendation engine 
input: movie 
...
Academic Mode
Accurately interpreting clickthrough 
data as implicit feedback 
Significant on 
two-tailed tests 
at a 95% 
confidence le...
Accurately interpreting clickthrough 
data as implicit feedback 
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembroo...
Accurately interpreting clickthrough 
data as implicit feedback
Evaluation Metrics 
● Mean Average Precision @ N 
○ probability of target result being in top N items 
● Mean Reciprocal R...
Optimizing search engines using 
clickthrough data 
Thorsten Joachims. Optimizing search engines using clickthrough data. ...
Optimizing search engines using 
clickthrough data
Query chains: learning to rank from 
implicit feedback 
Filip Radlinski and Thorsten 
Joachims. Query chains: learning 
to...
On Caption Bias in Interleaving 
Experiments 
Katja Hofmann, Fritz Behr, and Filip Radlinski: On Caption Bias in Interleav...
On Caption Bias in Interleaving 
Experiments
Fighting Search Engine Amnesia: 
Reranking Repeated Results 
In this paper, we observed that the same results are often sh...
Challenges
Diversification
Group recommendations
Context-aware recommendations 
Time of day 
Season 
Location 
Mood Device
Serious 
recommenders and search? 
Get in touch! 
@synopsitv @jsuchal
Rank all the things!
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Rank all the things!

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Talk from Conversion Meetup '13

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Rank all the things!

  1. 1. Rank all the things! @jsuchal @SynopsiTV
  2. 2. Blogs, newsletters Courses, training How do you learn things? Conferences Work
  3. 3. Research papers?
  4. 4. WHY NOT?
  5. 5. “It’s not useful for the real-world.” WHY NOT? “I wouldn’t understand any of that.”
  6. 6. About me PhD dropout FIIT STU Bratislava foaf.sk, otvorenezmluvy.sk, govdata.sk sme.sk news recommender developer @ SynopsiTV
  7. 7. My workflow
  8. 8. My workflow MAGIC! MAGIC! MAGIC!
  9. 9. Search vs. recommender engine Search engine input: query output: list of results Recommendation engine input: movie output: list of similar movies
  10. 10. Academic Mode
  11. 11. Accurately interpreting clickthrough data as implicit feedback Significant on two-tailed tests at a 95% confidence level !!! 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.
  12. 12. 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.
  13. 13. Accurately interpreting clickthrough data as implicit feedback
  14. 14. Evaluation 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
  15. 15. 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.
  16. 16. Optimizing search engines using clickthrough data
  17. 17. 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.
  18. 18. On Caption Bias in Interleaving Experiments Katja Hofmann, Fritz Behr, and Filip Radlinski: On Caption Bias in Interleaving Experiments In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM) 2012
  19. 19. On Caption Bias in Interleaving Experiments
  20. 20. Fighting Search Engine Amnesia: Reranking Repeated Results 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. 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.
  21. 21. Challenges
  22. 22. Diversification
  23. 23. Group recommendations
  24. 24. Context-aware recommendations Time of day Season Location Mood Device
  25. 25. Serious recommenders and search? Get in touch! @synopsitv @jsuchal

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