From federated to aggregated search

From federated to aggregated search Fernando Diaz, Mounia Lalmas and Milad Shokouhi [email_address] [email_address] [email_address]

Outline
Introduction and Terminology
Architecture
Resource Representation
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Introduction
What is federated search?
What is aggregated search?
Motivations
Challenges
Relationships

A classical example of federated search www.theeuropeanlibrary.org Collections to be searched One query

A classical example of federated search www.theeuropeanlibrary.org Merged list of results

Motivation for federated search
Search a number of independent collections, with a focus on hidden web collections
Collections not easily crawlable (and often should not)
Access to up-to-date information and data
Parallel search over several collections
Effective tool for enterprise and digital library environments

Challenges for federated search
How to represent collections, so that to know what documents each contain?
How to select the collection(s) to be searched for relevant documents?
How to merge results retrieved from several collections, to return one list of results to the users?
Cooperative environment
Uncooperative environment

From federated search to aggregated search
“ Federated search on the web”
Peer-to-peer network connects distributed peers (usually for file sharing), where each peer can be both server and client
Metasearch engine combines the results of different search engines into a single result list
Vertical search – also known as aggregated search – add the top-ranked results from relevant verticals (e.g. images, videos, maps) to typical web search results

A classical example of aggregated search News Homepage Wikipedia Real-time results Video Twitter Structured Data

Motivation for aggregated search
Increasingly different types of information being available, sough and relevant
e.g. news, image, wiki, video, audio, blog, map, tweet
Search engine allows accessing these through so-called verticals
Two “ways” to search
Users can directly search the verticals
Or rely on so called aggregated search
Google universal search 2007 : [ … ] search across all its content sources, compare and rank all the information in real time, and deliver a single, integrated set of search results [ … ] will incorporate information from a variety of previously separate sources – including videos, images, news, maps, books, and websites – into a single set of results. http://www.google.com/intl/en/press/pressrel/universalsearch_20070516.html

Motivation for aggregated search (Arguello et al , 09) 25K editorially classified queries

Motivation for aggregated search

Challenges in aggregated search
Extremely heterogeneous collections
What is/are the vertical intent(s)?
And
Handling ambiguous (query | vertical) intent
Handling non-stationary intent (e.g. news, local)
How many results from each to return and where to position them in the result page?
Slotting results
Users looking at 1 st result page
Page optimization and its evaluation

Ambiguous non-stationary intent Query - Travel - Molusk - Paul Vertical - Wikipedia - News - Image

Recap – Introduction federated search aggregated search heterogeneity low high scale (documents, users) small large user feedback little a lot

Terminology
federated search, distributed information retrieval, data fusion, aggregated search, universal search, peer-to-peer network
resource, vertical, database, collection, source, server, domain, genre
merging, blending, fusion, aggregation, slotted, tiled

Problem definition Present the “querier” with a summary of search results from one or more resources.

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

Peer-to-peer network Peer Directory Server

Peer to Peer (P2P) networks
Broker-based
Single centralized broker with documents lists shared from peer (e.g. Napster, original version )
Decentralized
Each peer acts as both client and server (e.g. Gnutella v0.4)
Structure-based
Use distributed hash tables (DHT) (e.g. Chord (Stocia et al, 03) )
Hierarchical
Use local directory services for routing and merging (e.g. Swapper.NET)

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

Federated search
Also known as distributed information retrieval (DIR) system
Provides one portal for searching information from multiple sources
corporate intranets, fee-based databases, library catalogues, internet resources, user-specific digital storage
Funnelback, Westlaw, FedStats, Cheshire, etc (see also http://federatedsearchblog.com/ )

http://funnelback.com/pdfs/brochures/enterprise.pdf

Metasearch User Metasearch engine Raw Query WWW Query Query Query Query

Metasearch
Search engine querying several different search engines and combines results from them (blended), or displays results separately (non-blended)
Does not crawl the web but rely on data gathered by other search engines
Dogpile,Metacrawler, Search.com, etc
( see http://www.cryer.co.uk/resources/searchengines/meta.htm )

Aggregated search User Angelina Jolie Results WWW Index (text) Query Query Query Query

Aggregated search
Specific to a web search engine
“ Increasingly” more than one type of information relevant to an information need
mostly web page + image, map, blog, etc
These types of information are indexed and ranked using dedicated approaches (verticals)
Presenting the results from verticals in an aggregated way believed to be more useful
All major search engines are doing some levels of aggregated search

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)

Data fusion
Search one collection
Document can be indexed in different ways
Title index, abstract index, etc (poly-representation)
Weighting scheme
Different retrieval models
Rankings generated by different retrieval models (or different document representations) merged to produce the final rank
Has often been shown to improve retrieval performance (TREC)

Terminology - Resource
Source
Server
Database
Collection (federated search)
Server
Vertical (aggregated search)
Domain
Genre

Terminology - Aggregation
Merging
Blending
Fusion
Slotted
Tiled

Aggregated search (tiled) http://au.alpha.yahoo.com/

Aggregated search (tiled) Naver.com

Aggregated search (slotted)

Others
Clustering
Faceted search
Multi-document summarization
Document generation
Entity search
(see special issue – in press – on “Current research in focused retrieval and result aggregation”, Journal of Information Retrieval (Trotman etal, 10))

Yippy – Clustering search engine from Vivisimo clusty.com

Faceted search

Multi-document summarization http://newsblaster.cs.columbia.edu/

“ Fictitious” document generation (Paris et al, 10)

Entity search http://sandbox.yahoo.com/Correlator

Recap
Shown the relations between federated, aggregated search, and others
Exposed the various terminologies used
In the rest of the tutorial, we concentrate on federated search and aggregated search
Focus is on “effective search”

Outline
Architecture
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Architecture: what are the general components of federated and aggregated search systems.

Federated search architecture

Aggregated search architecture
Pre-retrieval aggregation: decide verticals before seeing results
Post-retrieval aggregation: decide verticals after seeing results
Pre-web aggregation: decide verticals before seeing web results
Post-web aggregation: decide verticals after seeing web results

Post-retrieval, pre-web

Pre and post-retrieval, pre-web

Outline
Architecture
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Resource representation: how to represent resources, so that we know what documents each contain.

Resource representation in federated search (Also known as resource summary/description)

Resource representation
Cooperative environments
Comprehensive term statistics
Collection size information
Uncooperative environments
Query-based sampling
Collection size estimation

Resource representation (cooperative environments)
STARTS Protocol (Gravano et al, 97)
Source metadata
Rich query language

Different types of term statistics
(Callan et al, 95; Gravano et al, 94a,b,99; Meng et al, 01; Yuwono and Lee, 97; Xu and Callan, 98; Zobel, 97 )
Anchor-text
HARP (Hawking and Thomas, 05)
Resource representation (cooperative environments)

Resource representation (uncooperative environments)
Query-based sampling ( Callan and Connell, 01 )
Select a query, probe collection
Download the top n documents
Select the next query, repeat
Query selector Query Sampled documents

Query selector
(Callan and Connell, 01)
Other resource description (ord)
Learned resource description (lrd)
Average tf, random , df, ctf
Query logs
(Craswell, 00; Shokouhi et al, 07d)
Focused probing
(Ipeirotis and Gravano, 02)

Adaptive sampling
(Shokouhi et al, 06a)
Rate of visiting new vocabulary
(Baillie et al, 06a)
Rate of sample quality improvement (reference query log)
(Caverlee et al, 06)
Proportional document ratio ( PD )
Proportional vocabulary ratio ( PV )
Vocabulary growth (VG)

Improving incomplete samples
Shrinkage (Ipeirotis, 04; Ipeirotis and Gravano, 04) : topically related collections should share similar terms
Q-pilot (Sugiura and Etzioni, 00) :
sampled documents + backlinks + front page

Capture-recapture ( Liu et al, 01)
Resource representation (Collection size estimation) Sample A (Capture) Sample B (recapture) http://www.dorlingkindersley-uk.co.uk/static/cs/uk/11/clipart/nature/image_nature040.html

Resource representation (Collection size estimation)

Multiple queries sampler
( Thomas and Hawking, 07 )
Random-walk sampler, and pool-based sampler
( Bar-Yossef and Gurevich, 06 )
Collection overlap estimation
( Shokouhi and Zobel, 07 )
Resource representation (Collection size estimation)

Resource representation (Updating summaries)
(Ipeirotis et al, 05)
(Shokouhi et al, 07a)

Resource representation in aggregated search
Vertical content
samples or access to vertical API
represents content supply
Vertical query logs
samples or access to historic vertical searches
represents content demand

Vertical content includes text NEWS

Vertical content includes structure SPORTS

Vertical content includes images IMAGES

Issues with vertical content
Dynamics
some vertical becomes stale fast
Heterogeneous content
heterogeneous ranking algorithms
Non-free text APIs
affects query-based sampling

Addressing content dynamics
sample most recently indexed documents
(Diaz 09)
assumes users more likely to be interested in recent content
in practice, only need a fraction of the corpus to perform well
(Konig et al, 09)

Addressing heterogeneous content
use text available with documents (e.g. captions)
manually map to surrogates (e.g. wikipedia pages)
(Arguello et al, 09) performance of two different methods of dealing with heterogeneous content

Vertical query logs
Queries issued directly to a vertical represent explicit vertical intent
Is similar to having a large body of labeled queries

Issues with vertical query logs
Dynamics
some verticals require temporally-sensitive sampling
for example, we do not want to sample news query logs for a whole year
Non-free text APIs
affects query modeling

Hybrid approaches
Should only sample documents likely to be useful for vertical selection/merging
e.g. a document which is never requested is not useful for representing a vertical
Suggests log-biased sampling
(Shokouhi et al, 06; Arguello et al, 09)

Recap – Resource representation federated search aggregated search Representation completeness low low-high Representation generation sampling/shared dictionaries sampling, API Freshness important critical

Outline
Architecture
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Resource selection: how to select the resource(s) to be searched for relevant documents.

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

“ Big-document” bag of word summaries
CORI ( Callan et al, 95)
GlOSS (Gravano et al, 94b)
CVV (Yuwono and Lee, 97)
Resource selection (Lexicon-based methods) Collection C Collection A Collection B Sampling Sampling Sampling Broker

Resource selection (Lexicon-based methods)
CORI
GlOSS

Sample documents with retained boundaries
ReDDE ( Si and Callan, 03a)
CRCS (Shokouhi, 07a)
SUSHI ( Thomas and Shokouhi, 09 )
Resource selection (Document-surrogate methods) Collection C Collection A Collection B Sampling Sampling Sampling Broker

Resource selection (Document-surrogate methods)
ReDDE
ReDDE assumes that the top-ranked sampled documents are relevant.
ReDDE estimates the size of collections by sample-resample
Assuming that all collections have the same size we have: yellow > blue > red
CRCS is inspired by ReDDE but assigns different probability of relevance based on document position: red > yellow, blue
Query Ranking Broker

SUSHI
Resource selection (Document-surrogate methods) http://www.monthly.se/nucleus/index.php?itemid=1464

SUSHI
Resource selection (Document-surrogate methods)
Different regression functions for each collection and query
Scores are comparable (estimated over the same index)
http://www.monthly.se/nucleus/index.php?itemid=1464

Utility maximization techniques
Model the search effectiveness
DTF (Nottelmann and Fuhr, 03) , UUM (Si and Callan, 04a) , RUM (Si and Callan, 05b)
Classification-based methods
Classify collections/queries for better selection
Classification-aware server selection (Ipeirotis and Gravano, 08) , classification-based resource selection (Arguello et al, 09a) , learning from past queries (Cetintas et al, 09)
Resource selection (Supervised methods)

Resource selection in aggregated Search
Content-based predictors
derived from (sampled) vertical content
Query string-based predictors
derived from query text, independent of any resource associated with a vertical
Query log-based predictors
derived from previous requests issued by users to the vertical portal

Content-based predictors
Distributed information retrieval (DIR) predictors
Simple result set predictors
numresults, score distributions, etc
(Diaz 09; Konig etal, 09)
Complex result set predictors
Clarity (Cronen-Townsend et al, 02)
Autocorrelation (Diaz, 07)
Many, many more (Hauff, 10)

Issues with content-based predictors
DIR (usually) assumes homogeneous content types
performance predictors (usually) assume text corpora
assumes ranking function consistency
between verticals
between vertical selector machine and vertical ranker machine
verticals have different dynamics (e.g. news vs. image)

String-based predictors
Dictionary lookups
terms correlated with a vertical (e.g., movie titles)
Regular expressions
patterns correlated with explicit vertical requests (e.g., obama news)
Named entities
automatically-detected entity types (e.g., geographic entities)

String-based predictors
Issues
curating lists and expressions (manual or automatic)
terms included in dictionary manually vetted for relevance
high precision/low recall

Log-based predictors
Classification approaches
(Beitzel etal 07; Li etal , 08)
Language model approaches
(Arguello etal, 09)
Issues
verticals with structured queries (e.g. local)
query logs with dynamics (e.g. news)
(Diaz, 09)

Comparing predictor performance (Arguello et al, 09)

Predictor cost
Pre-retrieval predictors
computed without sending the query to the vertical
no network cost
Post-retrieval predictors
computed on the results from the vertical
requires vertical support of web scale query traffic
incurs network latency
can be mitigated with vertical content caches

Combining predictors
Use predictors as features for a machine-learned model
Training data
editorial data
behavioral data (e.g. clicks)
other vertical data
(Diaz, 09; Arguello etal, 09; Konig etal, 09)

Editorial data
Data: <query,vertical,{+,-}>
Features: predictors based on f(query,vertical)
Models:
log-linear (Arguello etal, 09)
boosted decision trees (Arguello etal, 10)

Combining predictors (Arguello etal, 09)

Click data
Data: <query,vertical,{click,skip}>, <query,vertical,click through rate>
Features: predictors based on f(query,vertical)
Models:
log-linear (Diaz, 09)
boosted decision trees (Konig etal, 09)

Gathering click data
Exploration bucket:
show suboptimal presentations in order to gather positive (and negative) click/skip data
Cold start problem:
without a basic model, the best exploration is random
Random exploration results in poor user experience

Gathering click data
Solutions
reduce impact to small fraction of traffic/users
train a basic high-precision non-click model (perhaps with editorial data)
Other issues
Presentation bias: different verticals have different click-through rates a priori
Position bias: different presentation positions have different click-through rates a priori

Click precision and recall (Konig etal, 09) ability to predict queries using thresholded click-through-rate to infer relevance

Non-target data have training data no data

Non-target data
Data: <query,source vertical,{+,-}>
Features: predictors based on f(query,target vertical)
Models:
generic model+adaptation
(Arguello etal, 10)

Non-target data
(Arguello etal, 10)

Generic model
Objective
train a single model that performs well for all source verticals
Assumption
if it performs well across all source verticals, it will perform well on the target vertical
(Arguello etal, 10)

Non-target data
(Arguello etal, 10)
adapted model

Adapted model
Objective
learn non-generic relationship between features and the target vertical
Assumption
can bootstrap from labels generated by the generic model
(Arguello etal, 10)

Non-target query classification
(Arguello etal, 10)
average precision on target query classification; red (blue) indicates statistically significant improvements (degradations) compared to the single predictor

Training set characteristics
What is the cost of generating training data
how much money?
how much time?
how many negative impressions as a result of exploration?
Are targets normalized ?
can we compare classifier output?

Training set cost summary

Online adaptation
Production vertical selection systems receive a variety of feedback signals
clicks, skips
reformulations
A machine-learned system can adjust predictions based on real time user feedback
very important for dynamic verticals
(Diaz, 09; Diaz and Arguello, 09)

Online adaptation
Passive feedback: adjust prediction/parameters in response to feedback
allows recovery from false positives
difficult to recover from false negatives
Active feedback/explore-exploit: opportunistically present suboptimal verticals for feedback
allows recovery from both errors
incurs exploration cost

Online adaptation
Issues
setting learning rate for dynamic intent verticals
normalizing feedback signal across verticals
resolving feedback and training signal (click≠relevance)

Recap – Resource selection

Outline
Architecture
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Resource presentation: how to return results retrieved from several resources to users.

Same source (web) different overlapped indexes
Document scores may not be available
Title, snippet, position and timestamps
D-WISE (Yuwono and Lee, 96)
Inquirus (Glover et al., 99)
SavvySearch (Dreilinger and Howe, 1997)
Result merging (Metasearch engines)

Same corpus
Different retrieval models
Document scores/positions available
Unsupervised techniques
CombSUM, CombMNZ (Fox and Shaw, 93, 94)
Borda fuse ( Aslam and Montague, 01 )
Supervised techniques
Bayes-fuse, weighted Borda fuse ( Aslam and Montague, 01 )
Segment-based fusion ( Lillis et al 06, 08; Shokouhi 07b)
Result merging (Data fusion)

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

CORI ( Callan et al, 95)
Normalized collection score + Normalized document score.
Result merging

Result merging
SSL (Si and Callan, 2003b)
A G B C D E F H Query Ranking Selected resources L R D F Q Broker

Result merging http://upload.wikimedia.org/wikipedia/en/1/13/Linear_regression.png Source-specific score Broker score

Multi-lingual result merging
SSL with logistic regression (Si and Callan, 05a; Si et al, 08)
Personalized metasearch
(Thomas, 08)
Merging overlapped collections
COSCO ( Hernandez and Kambhampati 05) :
exact duplicates
GHV ( Bernstein et al, 06; Shokouhi et al, 07b) :
exact/near duplicates
Result merging - Miscellaneous scenarios

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)

Designers of aggregated search interfaces should account for the aggregation styles
for both, vertical intent key for deciding on position and type of “vertical” results
slotted  accurate estimation of the best position of “vertical” result
tiled  accurate selection of the type of “vertical” result
Slotted vs tiled

Recap – Result presentation federated search aggregated search Content type homogenous (text documents) heterogeneous Document scores depends on environment heterogeneous Oracle centralized index none

Outline
Architecture
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Evaluation Evaluation: how to measure the effectiveness of federated and aggregated search systems.

CTF ratio ( Callan and Connell, 01)
Spearman rank correlation coefficient (SRCC), ( Callan and Connell, 01)
Kullback-Leibler divergence (KL) (Baillie et al, 06b; Ipeirotis et al, 2005) , topical KL ( Baillie et al, 09)
Predictive likelihood ( Baillie et al, 06a)
Resource representation (summaries) evaluation – Federated search

Resource selection evaluation – Federated search

Result merging evaluation – Federated search
Oracle
Correct merging (centralized index ranking) ( Hawking and Thistlewaite, 99)
Perfect merging (ordered by relevance labels) (H awking and Thistlewaite, 99)
Metrics
Precision
Correct matches ( Chakravarthy and Haase, 95)

Vertical Selection Evaluation – Aggregated search
Majority of publications focus on single vertical selection
vertical accuracy, precision, recall
Evaluation data
editorial data
behavioral data
single vertical selection

Editorial data
Guidelines
judge relevance based on vertical results (implicit judging of retrieval/content quality)
judge relevance based on vertical description (assumes idealized retrieval/content quality)
Evaluation metric derived from binary or graded relevance judgments
(Arguello etal, 09; Arguello et al, 10)

Behavioral data
Inference relevance from behavioral data (e.g. click data)
Evaluation metric
regression error on predicted CTR
infer binary or graded relevance
(Diaz, 09; Konig etal , 09)

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

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)

Recap – Evaluation federated search aggregated search Editorial data document relevance judgments query labels Behavioral data none critical

Outline
Architecture
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Open problems in federated search
Beyond big document
Classification-based server selection (Arguello et al, 09a)
Topic modeling
Query expansion
Previous techniques had little success (Ogilvie and Callan, 01; Shokouhi et al, 09)
Evaluating federated search
Confounding factors
Federated search in other context
Blog Search (Elsas et al, 08; Seo and Croft, 08)
Effective merging
Supervised techniques

Open problems in aggregated search
Evaluation metrics
slotted presentation
tiled presentation
metrics based on behavioral signals
Models for multiple verticals
Minimizing the cost for new verticals, markets

Outline
Architecture
Resource Selection
Result Presentation
Evaluation
Open Problems
Bibliography

Bibliography
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From federated to aggregated search

by Mounia Lalmas

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