From federated to aggregated search
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SIGIR 2010 Tutorial, with Fernando Diaz & Milad Shokouhi
![From federated to aggregated search Fernando Diaz, Mounia Lalmas and Milad Shokouhi [email_address] [email_address] [email_address]](https://image.slidesharecdn.com/sigir2010finalcorrectmounia-120304112902-phpapp01/85/From-federated-to-aggregated-search-1-320.jpg)
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From federated to aggregated search
- 1. From federated to aggregated search Fernando Diaz, Mounia Lalmas and Milad Shokouhi [email_address] [email_address] [email_address]
- 5. A classical example of federated search www.theeuropeanlibrary.org Collections to be searched One query
- 6. A classical example of federated search www.theeuropeanlibrary.org Merged list of results
- 10. A classical example of aggregated search News Homepage Wikipedia Real-time results Video Twitter Structured Data
- 12. Motivation for aggregated search (Arguello et al , 09) 25K editorially classified queries
- 13. Motivation for aggregated search
- 14. Motivation for aggregated search
- 16. Ambiguous non-stationary intent Query - Travel - Molusk - Paul Vertical - Wikipedia - News - Image
- 17. Recap – Introduction federated search aggregated search heterogeneity low high scale (documents, users) small large user feedback little a lot
- 19. Problem definition Present the “querier” with a summary of search results from one or more resources.
- 20. General architecture User Search Interface/ Portal/ Broker Source/ Server/ Vertical Source/ Server/ Vertical Source/ Server/ Vertical Source/ Server/ Vertical Raw Query Source/ Server/ Vertical Query Query Query Query Query
- 21. Peer-to-peer network Peer Directory Server
- 23. Federated search Query Broker Collection A Query Query Query Query Query Collection B Collection C Collection D Collection E Sum A Sum B Sum C Sum D Sum E Merged results
- 26. Metasearch User Metasearch engine Raw Query WWW Query Query Query Query
- 28. Aggregated search User Angelina Jolie Results WWW Index (text) Query Query Query Query
- 30. Data fusion Query GOV2 BM25 KL Inquery Anchor only Title only One document collection Different document representations Different retrieval models Merging One ranked list of result (merged) (e.g. Voorhees etal, 95)
- 34. Aggregated search (tiled) http://au.alpha.yahoo.com/
- 35. Aggregated search (tiled) Naver.com
- 38. Yippy – Clustering search engine from Vivisimo clusty.com
- 39. Faceted search
- 41. “ Fictitious” document generation (Paris et al, 10)
- 45. Architecture: what are the general components of federated and aggregated search systems.
- 49. Pre and post-retrieval, pre-web
- 51. Resource representation: how to represent resources, so that we know what documents each contain.
- 52. Resource representation in federated search (Also known as resource summary/description)
- 61. Resource representation (Collection size estimation)
- 65. Vertical content includes text NEWS
- 66. Vertical content includes structure SPORTS
- 67. Vertical content includes images IMAGES
- 74. Recap – Resource representation federated search aggregated search Representation completeness low low-high Representation generation sampling/shared dictionaries sampling, API Freshness important critical
- 76. Resource selection: how to select the resource(s) to be searched for relevant documents.
- 77. Resource selection for federated search Query Broker Collection A Query Query Query Collection B Collection C Collection D Collection E Sum A Sum B Sum C Sum D Sum E
- 92. Comparing predictor performance (Arguello et al, 09)
- 96. Combining predictors (Arguello etal, 09)
- 100. Click precision and recall (Konig etal, 09) ability to predict queries using thresholded click-through-rate to infer relevance
- 101. Non-target data have training data no data
- 109. Training set cost summary
- 113. Recap – Resource selection
- 115. Resource presentation: how to return results retrieved from several resources to users.
- 118. Result merging in federated search User Broker Collection A Query Query Collection B Collection C Collection D Collection E Sum A Sum B Sum C Sum D Sum E Merged results Query
- 121. Result merging http://upload.wikimedia.org/wikipedia/en/1/13/Linear_regression.png Source-specific score Broker score
- 123. Images on top Images in the middle Images at the bottom Images at top-right Images on the left Images at the bottom-right Slotted vs tiled result presentation 3 verticals 3 positions 3 degree of vertical intents (Sushmita et al, 10)
- 125. Recap – Result presentation federated search aggregated search Content type homogenous (text documents) heterogeneous Document scores depends on environment heterogeneous Oracle centralized index none
- 127. Evaluation Evaluation: how to measure the effectiveness of federated and aggregated search systems.
- 129. Resource selection evaluation – Federated search
- 134. Test collections (a la TREC) * There are on an average more than 100 events/shots contained in each video clip (document) (Zhou & Lalmas, 10) Statistics on Topics number of topics 150 average rel docs per topic 110.3 average rel verticals per topic 1.75 ratio of “General Web” topics 29.3% ratio of topics with two vertical intents 66.7% ratio of topics with more than two vertical intents 4.0% quantity/media text image video total size (G) 2125 41.1 445.5 2611.6 number of documents 86,186,315 670,439 1,253* 86,858,007
- 135. ImageCLEF photo retrieval track …… TREC web track INEX ad-hoc track TREC blog track topic t 1 doc d 1 d 2 d 3 … d n judgment R N R … R …… Blog Vertical Reference (Encyclopedia) Vertical Image Vertical General Web Vertical Shopping Vertical topic t 1 doc d 1 d 2 … d V1 judgment R N … R vertical V 1 V 2 d 1 d 2 … d V2 N N … R …… V k d 1 d 2 … d Vk N N … N t 1 existing test collections (simulated) verticals Test collections (a la TREC)
- 136. Recap – Evaluation federated search aggregated search Editorial data document relevance judgments query labels Behavioral data none critical
Notas del editor
- Add URL
- MILAD: Anchor text should not be THERE (you said it – please updated) MILAD: there was a comment from Andrew Trotman (we can ignore) about cooperative search engines. Anything you want to add about this (as I said we can safely ignore)
- There was a comment about Amdox (Yellow Page): Mliad???
- Say why some are underlined.
- Formula does not print
- Slide did not print well (stuff missing)
- Milad you said “Collection overlap estimation” was misplaced here.
- I have a comment here that says add the MJ slide
- Server vs collection here – does it matter at the end? Would be nice to have collection here
- Server vs collection
- Server vs collection
- Milad, you did speak quite a bit here, so maybe add something more?
- I have a comment here: KDD cup?
- All should be in % (or at least same format) Text needed here.
- Say in some text what is combined here.
- For other issues here, I have as comment add refs.
- I have as comment here “predict newsworthiness of queries”
- Say what C and D are.
- Check E and F – something was not correct.
- This slide does not print
- This slide does not print.
- CTR is full