Search Tasks, Proactive Search & Digital Assistants

ML Meetup

RishabhMehrotra

Motivation

Search Tasks

From Sessionsto Tasks

Multitasking

User Groups

TopicalHeterogeneity

ExtractingTasks &Subtasks

Task Extraction

SubtaskExtraction

Hierarchies ofTasks &Subtasks

Search Tasks, Proactive Search & DigitalAssistants

Rishabh Mehrotra

University College [email protected]

Machine Learning Meetup, Gurgaon

Saturday 25th June, 2016

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Modelling Search Tasks & Behaviors

1 Motivation

2 From Sessions to Tasks

3 Extracting Tasks & Subtasks

4 Hierarchies of Tasks & Subtasks

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Evolution of Web Search

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Search is Everywhere

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Web Search Volume

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Next Generation Systems

Towards Task-based SearchSystems

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Slight Detour - Proactive IR & Digital Assistants

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction



Information Cards:

From Queries to Cards (Shokouhi et al. SIGIR’15)Modelling User Interests for Zero-query Ranking (Yang etal. ECIR’16)

Intelligent Assistants:

Understanding User Satisfaction with Intelligent Assistants(Kiseleva et al. CHIIR’16)Automatic Online Evaluation of Intelligent Assistants(Jiang et al. WWW’15)

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction



Modelling user interaction on Information Cards:

Click-based (pseudo) relevance labels may not beappropriate for evaluating all types of cards.

Features

Reactive HistoryProactive HistoryLexical/Topical FeaturesLocal/Temporal FeaturesConstant Features

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction



ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Slight Detour - Proactive IR & Digital Assistants1

1Understanding User Satisfaction with Intelligent Assistants, Kiseleva etal. ACM CHIIR 2016

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Putting it together

Digital Assistants - entry points for all web activities

Goal is to move towards Task Completion

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Understanding Search Tasks

Search Tasks

An (atomic?) information need that may result in issuing ofone or more queries.

Image from Verma et al. 2014

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Why Search Tasks

Image from Wang et al. 2014

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Outline

Part 1 Understanding Search Sessions

Part 2 Extracting Tasks & Subtasks

Part 3 Hierarchies of Tasks & Subtasks

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Understanding Search Behavior

IR research = sessions, topics, queries

We focus on:

1 Multitasking

2 User groups based on multitasking

3 Topical heterogeneity

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Research Questions

1 RQ1: Extent of multi-tasking in search sessions?

2 RQ2: Evidence of user-level heterogeneity based on taskbehavior?

1 RQ2.1: Does search task effort vary across user groups?2 RQ2.2: Association between users’ interests and task

multiplicity?

3 RQ3: Characterizing various heterogeneities in searchbehavior.

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Data Context

No of Queries 620M

No of Sessions 190M

No of Users 2M

Avg No of queries per session 3.18

Avg no of sessions per user 76.01

Avg no of tasks per session 2.08

Table: Data summary

Time Query SessionID TaskID Topic

05/29/2012 14:06:04 adele songs 1 1 Arts05/29/2012 14:11:49 wedding venue 1 2 Society05/29/2012 14:12:01 video download 1 3 Arts

05/29/2012 14:06:04 Obama care 2 4 News05/29/2012 14:11:49 running shoes 2 5 Shopping05/29/2012 14:12:01 sports shoes 2 5 Shopping05/29/2012 14:22:12 wedding cards 2 2 Society

Table: Sample search sessions

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Extent of Multitasking

Quantifying the extent of multi-tasking

0%

20%

40%

60%

80%

100%

% se

ssions

No of tasks in a session

EXTENT OF MULTITASKING IN SESSIONS

2 3 4 5 6 7 8 9 10 10+ 1 110629692

0

10

20

30

40

50

60

No of Users (In %)

Avg Number of Tasks Performed in a Session 1 2 3 4 5 6 7 8 9 10 10+

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Task Extraction

SubtaskExtraction


User Groups

Focussed Users

Mul--‐taskers

Supertaskers

0%

20%

40%

60%

80%

100%

% of U

ser P

opula-

on

Types of User Groups

TASK CENTRIC USER GROUPS

Focussed Mul--‐Taskers Supertaskers

Figure: User groups based on multi-tasking behaviors

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


User Groups: Example Sessions

User Type Example Queries from a Typical Session

Focussed User

”test guide.com”, ”CNA Practice Test”, ”CNA StateBoard Exam”, ”CNA Testing Schedule and Loca-tions”, ”CNA State Board Practice Test”, ”CNAPractice Test 2014”, CNA 50 Questions Test”, ”FreeGED Practice Test 2014”

Multi-Tasking User”Gravity FSX 2.0”, ”Full Suspension MountainBikes”, ”Walmart Cards”, ”Walmart Instant CardApplication”, ”Gravity FSX 2.0 price”, ”full suspen-sion bike sale”

Supertasking User

”hairstyles for women over 50”, ”thin wavy hairstylesfor women”, ”facebook”, ”fb sign in”, pulledpork crock pot recipe easy”, ”Slow-Roasted PulledPork”, ”barefoot contessa”, ”miley cyrus hair styles”,”hairstyler.com”

Table: Example query sessions from the different user groups.

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Effort Metrics across User Groups

TTFC: Time to First Click TTLC: Time to Last ClickPCC: Page Click Count PgCC: Pagination Click Count

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

TTFC TTLC PCC PgCC

Focussed

Multi-taskers

Super-taskers

Stan

dar

dis

ed s

core

s

Figure: User groups based on multi-tasking behaviors

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Topical Heterogeneity

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09

Shopping

Home

Kids

Health

Extent of Mul,tasking

(a) Focused

0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65 0.66

News

Sports

Arts

Kids


(b) Multitasker

0.88 0.9 0.92 0.94 0.96

News

Sports

Arts

Society


(c) Supertasker

0 0.1 0.2 0.3 0.4 0.5

Arts

Adult

Games

Computers

Extent of Single-‐Tasking

(d) Focused

-‐0.6 -‐0.5 -‐0.4 -‐0.3 -‐0.2 -‐0.1 0

Business

Adult

Computers

Games


(e) Multitasker

-‐0.86 -‐0.84 -‐0.82 -‐0.8 -‐0.78 -‐0.76 -‐0.74

Business

Home

Computers

Games


(f) Supertasker

Figure: Top topics prone to multi-tasking (Top) and single-tasking(Bottom) across different user groups.

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Takeaways2

Sessions should NOT be the focal units in IR

More than 70% of sessions are multi-task

Users differ based on their multi-tasking habits

Search effort varies across user groups

Some topics prone to multi-tasking than others

2R. Mehrotra, P. Bhattacharya, E. Yilmaz; Characterizing UsersMulti-Tasking Behavior in Web Search, at ACM SIGIR ConferenceCHIIR 2016

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Outline




ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Extracting Tasks & Subtasks

1 Task extraction

2 Subtask extraction

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Extracting Search Tasks

Various proposed strategies:

Clustering session based queries [Lucchese, WSDM’11]

Structured Learning Approach [Wang, WWW’13]

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction



Various proposed strategies:

Hawkes Process based Task Extraction [Li, KDD’14]

Entity-based Task Extraction [Verma, CIKM’14][White,CIKM’14]

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction



Problems abound:

Link query to on-going task = long chains

Impure tasks

Pairwise predictions need extra post-processing

Rely on large corpus of pre-tagged queries

Do not aggregate across users

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Random Fields based Task Extraction3

Propose a latent variable model for extracting tasks:

Assumes k-underlying search tasks

Places a Markov Random Field over the latent task layer

Assumes access to a task-oracle

Each latent task is a multinomial distribution over queryterms

3R. Mehrotra, E. Yilmaz; Extracting Search Tasks via Task RandomField Model, SIGIR 2016 (under review)

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Random Fields based Task Extraction

Figure: The proposed task-RF model for extracting search tasks.

The generative process:

Draw task proportions vector θ ∼ Dir(α)

Draw task labels z for all queries from the jointdistribution defined in Eq.(1)

For each query (qi ) in the session:

For each query term (wqi ,j)draw wqi ,j ∼ multi(βzi )

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction



A query marginal factorizes into its query terms:

p(q|z , β) =

Mq∏j=1

p(wj |z , β) (1)

The joint distribution of θ, z , q and w can be written as:

p(θ, z ,w |α, β, λ) = p(θ|α)p(z |θ, λ)N∏i=1

Mqi∏j=1

p(wqi ,j |zi , β) (2)

Under the MRF model, the joint probability of all taskassignments z = zi

Ni=1 can be written as:

p(z |θ, λ) =1

A(θ, λ)

N∏i=1

p(zi |θ) exp{λ∑

(m,n)∈E

1(zm = zn)} (3)

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction



Task Oracle: classifier which predicts whether or not 2 queriesbelong to the same task.

Query Based Featurescosine cosine similarity between the term sets of the queries

edit norm edit distance between query strings

Jac Jaccard coeff between the term sets of the queries

Term proportion of common terms between the queries

url-term-match-sum∑

u∈URLi m(qj , u) +∑

u∈URLj m(qi , u)

url-term-match-max maxu∈URLim(qj , u) + maxu∈URLjm(qi , u)

Embedding cosine distance between embedding vectors of the two queries

URL Based Featuresdom-match the number of domain matches between URLs from both queries

Min-edit-U Minimum edit distance between all URL pairs from the queries

Avg-edit-U Average edit distance between all URL pairs from the queries

Jac-U-min Minimum Jaccard coefficient between all URL pairs from the queries

Jac-U-avg Average Jaccard coefficient between all URL pairs from the queries

Table: Features used by the classifier.

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Extracting Tasks & Subtasks

1 Task extraction

2 Subtask extraction

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Subtask Extraction4

Complex task decompose to more focussed subtasks

Number of subtasks is unknown

Given: collection of on-task queries

Couple Bayesian Nonparametrics & Word Embeddings

4Subtask Extraction: R. Mehrotra, P. Bhattacharya, E. Yilmaz; ExploitingDistributional Representations with Distance Dependent CRPs for ExtractingSub-Tasks. Accepted at NAACL 2016

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Chinese Restaurant Process

p(zi = j‖D, α) ∝{f (dij) if j 6= i

α if j = i

1 For each query i ∈ [1,N], draw assignmentzi ∼ dist − CRP(α, f ,D).

2 For each sub-task, k ∈ {1, ....}, draw a parameter θ∗k ∼ G0.

3 For each query i ∈ [1,N],1 If ci /∈ z∗q1:N

, set the parameter for the i th query to θi = θqi .Otherwise draw the parameter from the base distribution,θi ∼ Dirichlet(λ).

2 Draw the ith query, wi ∼ Mult(M, θi ).

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Quantifying Task based Distances

Task: plan a weddingSample queries: wedding planning, wedding checklist, bridaldresses, wedding cards

Leverage word embeddings.

Classify each word as background word orsubtask-specific word.

Use a weighted combination of their embedding vectors toencode a query’s vector:

Vq =1

nterms

∑i

nqtiΣqnq

Vti (4)

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Evaluating Quality of Subtasks

Qualitative Analysis:Proposed Approach LDA

sub-task 1 sub-task 2 sub-task 3 sub-task 1 sub-task 2 sub-task 3

wedding hairstyles used wedding dresses wedding card holders wedding insurance christian wedding vows make your own wedding invitations

wedding hair dos colorful bridal gowns indian wedding program cards destination wedding dresses brides wedding cakes pictures

curly wedding hairstyles preowned wedding dresses wedding program paper wedding planning book cheap wedding dresses planners

pictures of wedding hair wedding attire regency wedding cards party supply stores tea length wedding dresses wedding colors

CRP TW

sub-task 1 sub-task 2 sub-task 3 sub-task 1 sub-task 2 sub-task 3

wedding planning kit wedding theme wedding insurance wedding insurance christian wedding vows cheap dresses

destination wedding wedding guide weddings in vegas destination wedding plus size bridesmaid wedding cakes pictures

wedding table decorations save the date ideas wedding cakes pictures financing wedding rings wedding colors pricing weddings

1938 wedding pictures wedding vacation planning a wedding party supply stores tea length wedding dresses macys wedding dresses

User Study:

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Takeaways

Latent variable model for task extraction based on Taskoracle

Extracts more coherent tasks

Nonparametric model for extracting subtasks

Couple nonparametric approaches with word embeddings

Human judgements & coherence estimates show improvedresults

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Outline




ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Hierarchies of Tasks & Subtasks

Complex search tasks places intense cognitive burden onthe userUsers need to explore domain-spaceFlat structure-less tasks lack insights about demarcationof subtasksProvides tremendous opportunity to contextually targetthe user (recommendations/advertisements)

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Constructing Bayesian Hierarchies

Tree as partitions & mixtures:Tree: partition over the group of queries (Q) where probabilityof a query group is defined via Dynamic Programming as

p(Q|T ) = πT f (Q) + (1− πt)∏

Ti∈ch(T )

p(leaves(Ti )|Ti ) (5)

Gamma-Poisson Model of Query Affinities:

Query-Term Based Affinity (r1)cosine cosine similarity between the term sets of the queries

edit norm edit distance between query strings

Jac Jaccard coeff between the term sets of the queries

Term proportion of common terms between the queries

URL Based Affinity (r2)Min-edit-U Minimum edit distance between all URL pairs from the queries

Avg-edit-U Average edit distance between all URL pairs from the queries

Jac-U-min Minimum Jaccard coefficient between all URL pairs from the queries

Jac-U-avg Average Jaccard coefficient between all URL pairs from the queries

Session/User Based Affinity (r3)Same-U if the two queries belong to the same user

Same-S if the two queries belong to the same session

Embedding Based Affinity (r4)Embedding cosine distance between embedding vectors of the two queries

Table: Query-Query Affinities.

f (Q) =i=3∏i=1

p(Ri |αi , βi ) (6)

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Constructing Bayesian Hierarchies

Forming Arbitrary Tree Structure:Bayesian Rose Trees (NIPS 2012)

Agglomerative Model Selection:

Bottom-up greedy agglomerative fashion

At each iteration: merges two of the trees in the forest

Which pair of tree to merge(& how) - merger that yieldsthe largest Bayes factor improvement

ML Meetup

RishabhMehrotra

Motivation

Search Tasks


Multitasking

User Groups



Task Extraction

SubtaskExtraction


Takeaways5

We can extract recursive hierarchies of tasks & subtasks

Nonparametric tree partition model for hierarchies

Allows for arbitrary structures

Improves term prediction & task extraction

Human labelled judgements show:

we extract coherent tasksSubtasks are validSubtasks are useful for completing the overall task

5R. Mehrotra, E. Yilmaz; Towards Hierarchies of Search Tasks & Subtasks,In Proceedings of the Poster Track WWW 2015

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Motivation

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Task Extraction

SubtaskExtraction


Thank You!

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SubtaskExtraction


References

R. Mehrotra, P. Bhattacharya, and E. Yilmaz.

Characterizing users’ multi-tasking behavior in web search.In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval,2016.


Deconstructing complex search tasks: A bayesian nonparametric approach for extracting sub-tasks.In Proceedings of NAACL-HLT, pages 599–605, 2016.


Sessions; tasks & topics - uncovering behavioral heterogeneities in online search behavior.In Proceedings of the 2016 39th International ACM SIGIR Conference on Research and Developmentin Information Retrieval. ACM, 2016.

R. Mehrotra and E. Yilmaz.

Towards hierarchies of search tasks & subtasks.In WWW 2015.

R. Mehrotra and E. Yilmaz.

Terms, topics & tasks: Enhanced user modelling for better personalization.In Proceedings of the 2015 International Conference on The Theory of Information Retrieval, pages131–140. ACM, 2015.

R. Mehrotra, E. Yilmaz, and M. Verma.

Task-based user modelling for personalization via probabilistic matrix factorization.In RecSys Posters, 2014.

D. Odijk, R. W. White, A. Hassan Awadallah, and S. T. Dumais.

Struggling and success in web search.In CIKM 2015.

White, Bennett, and Dumais.

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2011.Identifying task-based sessions in search engine query logs.In Proceedings of the fourth ACM international conference on Web search and data mining, pages277–286. ACM.

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2015a.Terms, topics & tasks: Enhanced user modelling for better personalization.In Proceedings of the 2015 International Conference on The Theory of Information Retrieval, pages131–140. ACM.

Rishabh Mehrotra and Emine Yilmaz.

2015b.Towards hierarchies of search tasks & subtasks.In Proceedings of the 24th International Conference on World Wide Web Companion, pages 73–74.International World Wide Web Conferences Steering Committee.

Rishabh Mehrotra, Scott Sanner, Wray Buntine, and Lexing Xie.

2013.Improving lda topic models for microblogs via tweet pooling and automatic labeling.In Proceedings of the 36th international ACM SIGIR conference on Research and development ininformation retrieval, pages 889–892. ACM.

Rishabh Mehrotra, Emine Yilmaz, and Manisha Verma.

2014.Task-based user modelling for personalization via probabilistic matrix factorization.In RecSys Posters.

Rishabh Mehrotra, Prasanta Bhattacharya, and Emine Yilmaz.

2016.Characterizing users’ multi-tasking behavior in web search.In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval,CHIIR ’16, pages 297–300, New York, NY, USA. ACM.

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean.

2013.Distributed representations of words and phrases and their compositionality.In Advances in neural information processing systems, pages 3111–3119.

Greg Pass, Abdur Chowdhury, and Cayley Torgeson.

2006.A picture of search.In InfoScale, volume 152, page 1.

Amanda Spink, Sherry Koshman, Minsoo Park, Chris Field, and Bernard J Jansen.

2005.Multitasking web search on vivisimo. com.In Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on,volume 2, pages 486–490. IEEE.

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2015.Understanding users cross-domain intentions in spoken dialog systems.In NIPS Workshop on Machine Learning for SLU and Interaction.

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2014.Entity oriented task extraction from query logs.In Proceedings of the 23rd ACM International Conference on Conference on Information andKnowledge Management, pages 1975–1978. ACM.

Chong Wang and David M Blei.

2009.Variational inference for the nested chinese restaurant process.In Advances in Neural Information Processing Systems, pages 1990–1998.

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2013.Learning to extract cross-session search tasks.In Proceedings of the 22nd international conference on World Wide Web, pages 1353–1364.International World Wide Web Conferences Steering Committee.

Search Tasks, Proactive Search & Digital Assistants

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