A good search engine is one when users come very regularly, type their queries, get their results, and leave quickly. With user engagement metrics from web analytics, these translate to a low dwell time, often low CTR, but a very high return rate. But user engagement is not just about this. User engagement is a multifaceted, complex phenomenon, giving rise to a number of approaches for its measurement: self-reporting (e.g. questionnaires); observational methods (e.g., facial expression analysis, desktop actions); and of course web analytics using online behavior metrics. These methods represent various trade-offs between the scale of the data analyzed and the depth of understanding. For instance, surveys are hardly scalable but offer rich, qualitative insights, whereas click data can be collected on a large-scale but are more difficult to analyze. This talk will present various efforts aiming at combining approaches to measure engagement and seeking to provide insights into what makes an engaging experience. The talk will focus of what makes users click or not click, and what this means in terms of user engagement. SIGIR 2013 Industry Track: Keynote by Ricardo Baeza-Yates - VP, Yahoo! Research Europe & Latin America

1. Ricardo Baeza-Yates
SIGIR 2013 – Industry Talk
An
Engaging
Click

2. Why is it important to engage users?
• In today’s wired world, users have enhanced expectations
about their interactions with technology
… resulting in increased competition amongst the
purveyors and designers of interactive systems.
• In addition to utilitarian factors, such as usability, we must
consider the hedonic and experiential factors of interacting
with technology, such as fun, fulfillment, play, and user
engagement.
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3. CTR and user engagement
CTR
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4. Multimedia search
activities often driven
by entertainment
needs, not by
information needs
CTR and entertainment driven search
(Slaney, 2011)
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5. I just wanted the phone number … I am totally satisfied J
CTR and factual needs
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6. This talk
What is user engagement?
What are the characteristics of user engagement?
How to measure user engagement?
What is an engaging click?
1. inter-session metric
2. online multi-tasking
3. serendipity
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Work on user
engagement across
web applications
Implications to search

8. http://thenextweb.com/asia/2013/05/03/kakao-talk-rolls-out-plus-friend-home-a-
revamped-platform-to-connect-users-with-their-favorite-brands/
Engagement is on everyone’s mind
http://socialbarrel.com/70-percent-of-brand-engagement-on-pinterest-come-from-users/
51032/
http://iactionable.com/user-engagement/
http://www.cio.com.au/article/459294/
heart_foundation_uses_gamification_drive_user_engagement/
http://www.localgov.co.uk/index.cfm?method=news.detail&id=109512
http://www.trefis.com/stock/lnkd/articles/179410/linkedin-makes-a-90-
million-bet-on-pulse-to-help-drive-user-engagement/2013-04-15
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9. What is user engagement?
User engagement is a quality of the user experience
that emphasizes the positive aspects of interaction –
in particular the fact of being captivated by the
technology (Attfield et al, 2011).
user feelings: happy, sad,
excited, …
emotional, cognitive and behavioural connection
that exists, at any point in time and over time, between
a user and a technological resource
user interactions: click,
read, comment, buy…
user mental states: involved,
lost, concentrated…
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10. Considerations in the measurement of
user engagement
• Short term (within session) and long term
(across multiple sessions)
• Laboratory vs. field studies
• Subjective vs. objective measurement
• Large scale (dwell time of 100,000 people) vs.
small scale (gaze patterns of 10 people)
• User engagement as process vs. product
One is not better than other; it depends on what is the aim.
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12. Characteristics of user engagement (I)
• Users must be focused to be engaged
• Distortions in the subjective perception of time used to
measure it
Focused attention
(Webster & Ho, 1997; O’Brien,
2008)
• Emotions experienced by user are intrinsically motivating
• Initial affective “hook” can induce a desire for exploration,
active discovery or participation
Positive Affect
(O’Brien & Toms, 2008)
• Sensory, visual appeal of interface stimulates user & promotes
focused attention
• Linked to design principles (e.g. symmetry, balance, saliency)
Aesthetics
(Jacques et al, 1995; O’Brien,
2008)
• People remember enjoyable, useful, engaging experiences
and want to repeat them
• Reflected in e.g. the propensity of users to recommend an
experience/a site/a product
Endurability
(Read, MacFarlane, & Casey,
2002; O’Brien, 2008)
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13. Characteristics of user engagement (II)
• Novelty, surprise, unfamiliarity and the unexpected
• Appeal to users’ curiosity; encourages inquisitive
behavior and promotes repeated engagement
Novelty
(Webster & Ho, 1997; O’Brien,
2008)
• Richness captures the growth potential of an activity
• Control captures the extent to which a person is able
to achieve this growth potential
Richness and control
(Jacques et al, 1995; Webster &
Ho, 1997)
• Trust is a necessary condition for user engagement
• Implicit contract among people and entities which is
more than technological
Reputation, trust and
expectation (Attfield et al,
2011)
• Difficulties in setting up “laboratory” style experiments
• Why should users engage?
Motivation, interests,
incentives, and
benefits (Jacques et al., 1995;
O’Brien & Toms, 2008)
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14. 14

15. Measuring user engagement
Measures
Characteristics
Self-
reported
engagement
Questionnaire, interview, report,
product reaction cards, think-aloud
Subjective
Short- and long-term
Lab and field
Small-scale
Product outcome
Cognitive
engagement
Task-based methods (time spent,
follow-on task)
Physiological measures (e.g. EEG,
SCL, fMRI, eye tracking, mouse-
tracking)
Objective
Short-term
Lab and field
Small-scale and large-
scale
Process outcome
Interaction
engagement
Web analytics
metrics + models
Objective
Short- and long-term
Field
Large-scale
Process outcome
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16. Large-scale measurements of
user engagement – Web analytics
Intra-session measures Inter-session measures
• Dwell time / session
duration
• Play time (video)
• (Mouse movement)
• Click through rate (CTR)
• Mouse movement
• Number of pages viewed
(click depth)
• Conversion rate (mostly for
e-commerce)
• Number of UCG
(comments)
• Fraction of return visits
• Time between visits (inter-session
time, absence time)
• Total view time per month (video)
• Lifetime value (number of actions)
• Number of sessions per unit of time
• Total usage time per unit of time
• Number of friends on site (social
networks)
• Number of UCG (comments)
• Intra-session engagement measures our success in attracting the user
to remain on site for as long as possible.
• Inter-session engagement can be measured directly or, for commercial
sites, by observing lifetime customer value.
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17. Dependency on task
• Engagement varies by task:
– user who accesses a website to check for emails
(goal-specific) has different engagement patterns
from one browsing for leisure.
• In (Yom-Tov et al, 2013), sessions in which
50% or more of the visited sites
belonged to the 5 most common sites
(for each user) were classified as goal-
specific.
– 38% sessions were goal-specific
– most users (92%) both goal-specific and non-goal-
specific sessions
– average downstream engagement in goal-
specific sessions was 0.16 vs. 0.2 during non-
goal-specific sessions
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18. 18

19. User engagement in search – “relevance”
• Click-through rate (CTR)
• Dwell time (search result)
• Time to first click
• Skipping
• Abandonment rate
• Number of query reformulations
• Search engine switching
• Interleaving
• Cumulative gain family of metrics
• …
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20. 20

21. Click vs cursor – heat-map
Estimate search result relevance
(Bing - Microsoft employees – 366,473 queries; 21,936 unique cookies;
7,500,429 cursor move or click)
the role of hovering
(Huang et al, 2011)
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22. Mouse movement – what can hovering tell
about relevance?
Click-through rate:
% of clicks when URL
Shown (per query)
Hover rate:
% hover over URL
(per query)
Unclicked hover:
Media time user hovers over
URL but no click (per query)
Max hover time:
Maximum time user hover
over a result (per SERP)
(Huang et al, 2011)
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23. • Domain: Yahoo! Answers Japan
• Study: Inter-session engagement metric
23
(Dupret & Lalmas, 2013)
If users find a web application interesting,
engaging or useful, they will return to it sooner.

24. Absence time andsurvivalanalysis
Easy to implement
and interpret
Can compare many
things in one go
No need to estimate
baselines
But need lots of data
to account for noise
(Dupret & Lalmas, 2013)
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Survival Analysis: high hazard rate = short absence

25. Using absence time to compare 6 ranking
functions (buckets) on Yahoo!Answers Japan
1. Returning relevant results is important, but is not enough to
keep returning to the search application
2. Clicks after the 5th results reflect poorer user experience;
users cannot find what they are looking for
3. No click means a bad user experience
4. Clicking lower in the ranking suggests more careful choice
from the user
5. Clicking at bottom is a sign of low quality overall ranking
6. Users finding their answers quickly (click sooner) return
sooner to the search application
7. Returning to the same search result page is a worse user
experience than reformulating the query.
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26. 26

27. Online multi-tasking
users spend more and more of their online session multi-tasking, e.g. emailing,
reading news, searching for information à ONLINE MULTI-TASKING
navigating between sites, using browser tabs, bookmarks, etc
seamless integration of social networks platforms into many services
leaving a site is
not a “bad thing!”
(fictitious navigation between sites within an online session)
181K users, 2 months browser
data, 600 sites, 4.8M sessions
• only 40% of the sessions have
no site revisitation
• hyperlinking, backpaging and
teleporting
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28. • Domain: 700+ web applications
• Study: Online multi-tasking
28
(Lehmann et al, 2013)
Online multi-tasking affects the way users interact
(or engage) with sites.

29. Online multi-tasking – and search
181K users, 2 months browser
data, 600 sites, 4.8M sessions
• only 40% of the sessions have
no site revisitation
• commonly accessed sites between visits à search 22%, navigation 12%, social 8%
• for some sites (e-commerce) same sites are accessed between visits à one task?
• no patterns for sites such as mail, social à anchor, habit?
• longer time between visits à a different task (new search)
• more vs less times spent at each revisit à increased vs shift of attention
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30. Navigating between sites –
hyperlinking, backpaging and teleporting
timestamp page navi
1346242507 1 T
1346242567 2 L
1346242627 3 L
(1346242687) 1 B
1346242687 4 L
1346242747 5 T
1346329147 6 L
(1346329207) 5 B
1346329207 7 L
(1346329267) 2 B
1346329267 8 L
2
3
1
4
8
5
76
click-tree 1 click-tree 2
1 - 2 - 3 - 1 - 4 - 5 - 6 - 5 - 7 - 2 - 8timestamp page referral
1346242507 1 -
1346242567 2 1
1346242627 3 2
1346242687 4 1
1346242747 5 -
1346329147 6 5
1346329207 7 5
1346329267 8 2
8
7
6
2
3
1
4
5
2
3
1
4
8
5
76
click-tree 1 click-tree 2
(a) Interaction data
click-stream
(b) Navigation path
click-stream
(c) Logical navigation
click-trees
(d) Interaction data
tree-stream
(e) Navigation path
tree-stream
Page [L] Hyperlinking [B] Backpaging [T] Teleportingn
Number of backpaging actions is an under-estimate!
(using browser back button, or user returns to one of several open tabs/windows)
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31. Revisitation and navigation patterns
auctionsites[complexattention]
●
●
●
●
●
●
●
●
●
101112
1 2 3 4 5 6 7 8 9
p-value = 0.24
m = 0.142
100% 67% 54% 46% 41% 35% 31% 29% 26%
searchsites[increasingattention]
●
●
●
●
● ●
●
●
●
10.811.011.2
1 2 3 4 5 6 7 8 9
100% 69% 54% 44% 38% 33% 29% 26% 23%
p-value < 0.05
m = 0.063
●
●
●
● ●
●
●
● ●
10.811.2
1 2 3 4 5 6 7 8 9
p-value < 0.05
100% 54% 36% 26% 20% 17% 14% 12% 10%
proportion of users
%oftotalpageviewsonsite%ofnavigationtype
Hyperlinking
mailsites[decreasingattention]
●
●
●
●
●
●
●
●
●
10111213
1 2 3 4 5 6 7 8 9
100% 62% 41% 29% 21% 16% 13% 10% 8%
p-value < 0.05
m = -0.288
averageattention
1 2 3 4 5 6 7 8 9
0.00.40.8
0.00.40.8
1 2 3 4 5 6 7 8 9
0.00.40.8
1 2 3 4 5 6 7 8 9
0.00.40.8
1 2 3 4 5 6 7 8 9
k [kth
visit on site] k [kth
visit on site] k [kth
visit on site] k [kth
visit on site]
Teleporting Backpaging
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32. Online multi-tasking – and web search
• 48% sites visited at least 9 times
• Revisitation “level” depends on site
• 10% users accessed a site 9+ times (23% for search
sites); 28% at least four times (44% for search sites)
• Activity on site decreases with each revisit but
activity on many search (and adult) sites increases
• Backpaging usually increases with each revisit but
hyperlinking remains important means to navigate
between sites
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33. 33

34. Networked user engagement:
engagement across a network of sites
• Large online providers (AOL, Google, Yahoo!,
MSN, etc.) offer not one service (site), but a
network of sites
• Each service is usually optimized individually, with
some effort to direct users between them
• Success of a service depends on itself, but also
on how it is reached from other services
(user traffic)
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35. Measuring downstream engagement
User session
Providersites
Downstream engagement
for site A
(% remaining session time)
Site A
35
(Yom-Tov etal, 2012)

36. Influential features
o Time of day
o Number of (non-image/non-video) links to Yahoo! sites in HTML body
o Average rank of Yahoo! links on page
o Number of (non-image/non-video) links to non-Yahoo! sites in HTML body
o Number of span tags (tags that allow adding style to content or
manipulating content, e.g. JavaScript)
o Link placements and number of Yahoo! links can influence downstream
engagement
o Not new, but here shown to hold also across sites
o Links to non-Yahoo! sites have a positive effect on downstream
engagement
o Possibly because when users are faced with abundance of outside links
they decide to focus their attention on a central content provider, rather than
visiting multitude of external sites
(Yom-Tov et al, under submission)

37. • Domain: social media (Yahoo! Answers and Wikipedia)
• Study: serendipity (in entity search)
37
(Bordino, Mejova & Lalmas, 2013)
Interesting search results may promote
serendipitous browsing.

38. Yahoo!Answers vs Wikipedia
community-driven question &
answer portal
• 67 336 144 questions &
261 770 047 answers
• January 1, 2010 –
December 31, 2011
• English-language
community-driven
encyclopedia
• 3 795 865 articles
• as of end of
December 2011
• English Wikipedia
curated
high-quality knowledge
variety of niche topics
minimally curated
opinions, gossip, personal info
variety of points of view
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Entity
Search
we build an entity-driven serendipitous search system
based on entity networks extracted from Wikipedia and
Yahoo! Answers
Serendipity finding something good or useful while not
specifically looking for it, serendipitous search
systems provide relevant and interesting results

39. Wikipedia
39
Yahoo! Answers
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40. Retrieval
Wikipedia Yahoo!
Answers
Combined
Precision @ 5 0.668 0.724 0.744
MAP 0.716 0.762 0.782
Justin Bieber, Nicki Minaj, Katy Perry, Shakira, Eminem, Lady Gaga,
Jose Mourinho, Selena Gomez, Kim Kardashian, Miley Cyrus, Robert
Pattinson, Adele %28singer%29, Steve Jobs, Osama bin Laden, Ron
Paul, Twitter, Facebook, Netflix, IPad, IPhone, Touchpad, Kindle,
Olympic Games, Cricket, FIFA, Tennis, Mount Everest, Eiffel Tower,
Oxford Street, Nubcrburgring, Haiti, Chile, Libya, Egypt, Middle East,
Earthquake, Oil spill, Tsunami, Subprime mortgage crisis, Bailout,
Terrorism, Asperger syndrome, McDonal's, Vitamin D, Appendicitis,
Cholera, Influenza, Pertussis, Vaccine, Childbirth
3 labels per query-result pair
gold standard quality control
Yahoo! Answers
Jon Rubinstein
Timothy Cook
Kane Kramer
Steve Wozniak
Jerry York
Wikipedia
System 7
PowerPC G4
SuperDrive
Power Macintosh
Power Computing Corp.
Steve Jobs
• Annotator agreement
(overlap): 0.85
• Average overlap in
top 5 results: <1
40
retrieve entities most related to a
query entity using random walk
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41. | relevant & unexpected | / | unexpected |
number of serendipitous results out of all
of the unexpected results retrieved
| relevant & unexpected | / | retrieved |
serendipitous out of all retrieved
41
Baseline
Data
Top:
5
en//es
that
occur
most
frequently
WP
0.63
(0.58)
in
top
5
search
from
Bing
and
Google
YA
0.69
(0.63)
Top
–WP:
same
as
above,
but
excluding
WP
0.63
(0.58)
Wikipedia
page
from
results
YA
0.70
(0.64)
Rel:
top
5
en//es
in
the
related
query
WP
0.64
(0.61)
sugges/ons
provided
by
Bing
and
Google
YA
0.70
(0.65)
Rel
+
Top:
union
of
Top
and
Rel
WP
0.61
(0.54)
YA
0.68
(0.57)
Serendipity “making fortunate discoveries by accident”
Serendipity = unexpectedness + relevance
“Expected” result baselines from web search
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42. Interestingness ≠ Relevance
Interesting > Relevant
Relevant > Interesting
Oil Spill à
Penguins in Sweaters WP
Robert Pattinson à
Water for Elephants WP
Lady Gaga à Britney Spears WP
Egypt à Cairo Conference WP
Netflix à Blu-ray Disc YA
Egypt à
Ptolemaic Kingdom WP & YA
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43. Similarity (Kendall’s tau-b) between result sets and reference ranking
43
Data
tau-­‐b
Which
result
is
more
WP
0.162
relevant
to
the
query?
YA
0.336
If
someone
is
interested
in
the
query,
would
WP
0.162
they
also
be
interested
in
the
result?
YA
0.312
Even
if
you
are
not
interested
in
the
query,
WP
0.139
is
the
result
interes;ng
to
you
personally?
YA
0.324
Would
you
learn
anything
new
about
WP
0.167
the
query
from
the
results
YA
0.307
Following (Arguello et al, 2011)
1. Labelers provide pairwise
comparisons between results
2. Combine into a reference ranking
3. Compare result ranking to optimal
ranking using Kendall’s tau
Assessing
“interestingness”
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44. 44

45. Take-away messages
• Search is not just about specific information needs
• People search for many other reasons
– Navigation
– Transaction
– Fun (ECIR 2012 workshop)
– Etc.
• Engagement in search is to view search activities as part of the
current overall task of a user
• We never know what we get if we are ready to explore
– Users do things that no one expects, not even them!
(like staying inside Yahoo! in spite of having many links to go elsewhere)
– So a link is not everything, for search too!
• Summarizing, we need to look at engagement in a broader way

46. Thank you
Acknowledgements: Mounia Lalmas, Jahnette Lehmann, George
Dupret, Ilaria Bordino, Yelena Mejova and Elad Yom-Tov.
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