Ricci, F. at ENTER2016 eTourism Conference

1. How do we Search?
Themes and Challenges
Francesco Ricci
Free University of Bozen-Bolzano
fricci@unibz.it

2. 2
Motivations
p Internet has made information
free and abundant – practically
everything is online
p Mobile devices and networks
have made global reach and continuous
connectivity widely available
p Cloud computing has put practically infinite
computing power and storage and a host of
sophisticated tools and applications to everyone’s
disposal, on an inexpensive, pay-as-you-go basis
p Internet content would be wasted if that
information could not be found, analyzed, and
exploited.
I C

3. Content
p How we process information
p The yin-yang of information seeking and
discovery
p Information search tools
p Personalization
p User preferences
p Contextualization
p Proactivity
p Groups
3

4. 4

5. Neuroscience and Information Overload
p Our brains (120 bits per second) are configured
to make a certain number of decisions per
day and once we reach that limit, we can’t make
any more
p Information processing makes us tired: we
can have trouble separating the trivial from the
important stuff (decision overload)
5

6. Attentional Filters Principles
p Change: when the brain detects a change the
information is sent to your consciousness
n Example: one new widget is shown on a page
that you often visit – you look at it
p Importance: something that is personally
important for you go through the attentional filter
n Example: in a long list of emails you can
immediately spot one coming from the
conference you submitted a paper to
p BUT we may fail to detect important
facts/information even shown in front of us:
inattentional blindness basketball demo
6

7. p He tries to capture our attention
p He makes us tired
p We always miss to capture some part of the
output content
7

8. Information Seeking and Discovery
8
Today's challenges for information search
originate from information discovery tasks

9. 9
Exploratory Search
Gary Marchionini: Exploratory search: from finding to understanding.
Commun. ACM 49(4): 41-46 (2006)

10. 10
Lookup: from needs to queries
p Information need -> query -> search engine ->
results -> browse OR query -> ...
Encoded by the
user into a queryInformation
need

11. 11
Taxonomy of Web search
p In the web context the "need behind the query" is often
not informational in nature
p [Broder, 2002] classifies web queries according to their
intent into 3 classes:
1. Navigational - GO: The immediate intent is to reach a
particular site (20%):
p q = facebook - probable target
http://www.facebook.com
2. Informational - KNOW: The intent is to acquire some
information assumed to be present on one or more
web pages (50%)
p q= canon 5d mkIII - probable target a page
reviewing canon 5d mkIII
3. Transactional - DO: The intent is to perform some
web-mediated activity (30%)
p q = hotel Vienna - probable target "Expedia"
Andrei Z. Broder: A taxonomy of web search. SIGIR Forum 36(2): 3-10 (2002)

12. Discovery: Musicovery Mobile
12

13. 13
http://njhotelsnearnyc.com/
Make it easier for the user to discover
useful services/information

14. Information Search Process
14Carol Collier Kuhlthau Information Search Process Rutgers University
Model of the Information Search Process
Tasks Initiation Selection Exploration Formulation Collection Presentation
----------------------------------------------------------------------------------------------------------------------------------------------------→
Feelings uncertainly optimism confusion clarity sense of satisfaction or
(affective) frustration direction/ disappointment
doubt confidence
Thoughts vague-------------------------------------→focused
(cognitive) -----------------------------------------------→
increased interest
Actions seeking relevant information----------------------------→seeking pertinent information
(physical) exploring documenting
by Carol Collier Kuhlthau

15. Information Search Tools
15
A search engine is just a tool not an
oracle – you must be able to use it and
accept its limits

16. Search engines
p Search engines are the primary tools people
use to find information on the web
p They focus on text and link analysis
p Exclusion of a site from search engines will
cut off the site from its intended audience.

17. Architecture of a Search Engine
Sec. 7.2.4
17

18. Complex Queries are never used
18
Advanced operators in queries are almost never used
Percentage of single vs. multiple word searches in various languages.
Source: http://www.keyworddiscovery.com/

19. Successful Queries are the Minority
19
Source: http://www.keyworddiscovery.com/

20. 20
Queries will disappear
Leverage a multiplicity of signals to get rid of queries

21. Personalization
21
The more people you have to serve the
less good the result is

22. 22
Guest
Francesco

23. Personalization in Search Engines
p Short term: capturing the features of the current
user context
p Long term: user’s preferences over an extended
period of time
p Individual vs Social
p Mostly using implicit preferences (clicks, past
queries)
n To extract interesting topics
n To identify pairwise preferences
p Used for: re-rank results, implicit query
expansion, disambiguation.
23
B. Smyth, M. Coyle, P. Briggs, K. McNally, M.P. O’Mahony. Collaboration, Reputation and
Recommender Systems in Social Web Search, in F. Ricci, L. Rokach and B. Shapira,
Recommender Systems Handbook, Springer US, 2015: 569-608.

24. Query Suggest
24

25. Movie Recommendation – YouTube
25
Recommendations account for about 60%
of all video clicks from the home page.

26. 1
2
3

27. 27
No personalization
Only inspiration and Search

28. 28

29. 29

30. 30

31. 31
No personalization
Just Search

32. Personalizing Maps
p Instead of generating one map for large numbers
of users, user profiling and implicit feedback
analysis can support creation of a different map
for each person
p What the user is doing with
the map?
p Different maps suit different
tasks, with respect to
features, layers, and controls
32
http://cacm.acm.org/magazines/2015/12/194625-personalizing-maps
Andrea Ballatore, Michela Bertolotto: Personalizing maps.
Commun. ACM 58(12): 68-74 (2015)

33. Map Personalization
33

34. User Feedback and Preference Learning
34

35. Last.fm – Preference Elicitation

36. Rating Recommendations
36

37. Alternative Methods
37
I like beaches
when traveling with
my family,
otherwise I prefer
high mountain
excursions
L. Blédaité and F. Ricci. Pairwise preferences elicitation and exploitation for
conversational collaborative filtering. In Proceedings of the 26th ACM Conference
on Hypertext & Social Media, pages 231–236. ACM, 2015.

38. Contextualization
38

39. Context-Aware Computing
p Gartner Top 10 strategic technology trends for IT
p "Context-aware computing is a style of computing in
which situational and environmental information about
people, places and things is used to anticipate
immediate needs and proactively
offer enriched, situation-aware
and usable content, functions
and experiences."
39http://www.gartner.com/it-glossary/context-aware-computing-2

40. Factors influencing Holiday Decision
Decision
Personal
Motivators
Personality
Disposable
Income
Health
Family
commitments
Past experience
Works
commitments
Hobbies and
interests
Knowledge of
potential
holidays
Lifestyle Attitudes,
opinions and
perceptions
Internal to the tourist External to the tourist
Availability of
products Advice of travel
agents
Information obtained
from tourism
organization and
media
Word-of-mouth
recommendations
Political restrictions:
visa, terrorism,
Health problems
Special promotion
and offers
Climate
[Swarbrooke & Horner, 2006]

41. Content and Context are Continuously
Changing
41
Same query – one day after

42. Issues
p Should the query result change if you enter the
same query twice?
p Will your user accept to find different results for
the same query at different times?
p When the query results should change?
p What can change in a query result?
42

43. Proactivity
43
You cannot win the game without
thinking ahead

44. Google Now
44
https://www.google.com/landing/now/

45. Issues
p Estimate not only if an information is useful but
also:
n When it is useful (timing)
n How the user should be prompted (GUI)
n How frequently the user should be recalled
n When the user will perceive the information as
more useful/relevant
45
Matthias Braunhofer, Francesco Ricci, Béatrice Lamche, Wolfgang Wörndl:
A Context-Aware Model for Proactive Recommender Systems in the
Tourism Domain. MobileHCI Adjunct 2015: 1070-1075

46. Groups
46

47. Groups
p Information search tools are usually designed to
provide support and content adapted to the
preferences of a single user
p In many situations the searched items are
consumed by a group of users
n A travel with friends
n A movie to watch with
the family during
Christmas holidays
n Music to be played in a
car for the passengers
47

48. Werecommend
First Mainstream Approach
p Creating the joint profile of a group of users
p We build a recommendation for this “mixed” user
p Issues
n The recommendations may be difficult to explain –
individual preferences are lost
n Recommendations are customized for a “user” that
is not in the group
n There is no well founded way to “combine” user
profiles – why averaging?
48
+ + =

49. Second Mainstream Approach
p Producing individual recommendations
p Then “aggregate” the recommendations:
p Issues
n How to optimally aggregate ranked lists of
recommendations?
n Is there any “best method”?
49

50. Decision Making Process
50D. Forsyth, Group Dynamics,
Wadsworth Publishing, 2013
Decision making –
preference aggregation
- is only one step

51. Multiparty Recommendation
Critiquing Critiquing
cuisine
atmosphere
price
Da Gennaro
cuisine
atmosphere
price
“I prefer a hipper
atmosphere” … 8^)
Che Maxim
Francesca Guzzi, Francesco Ricci, Robin D. Burke: Interactive multi-party
critiquing for group recommendation. RecSys 2011: 265-268

52. 52

53. Budgeted Social Choice
p Example: your brochure can list a maximum of
ten services that jointly maximize consumer
satisfaction
p Select the combination of services such that the
sum of the satisfaction of the users, when they
select the best option for them, is maximized
p Comparison
n Recommender Systems: one option one user
n Group Recommender Systems: one option
many users
n Budgeted Social Choice: many options many
users
53Tyler Lu, Craig Boutilier: Budgeted Social Choice: From Consensus to
Personalized Decision Making. IJCAI 2011: 280-286

54. Social Search: HeyStaks.com
p People who share
interests issue similar
search queries, select
similar results and visit
similar websites
p HeyStaks analyzes user
behavior to identify
communities of like-
minded people
p Promote content based on
selected stack (topic) and
social relations.
54
B. Smyth, M. Coyle, P. Briggs, K. McNally, M.P. O’Mahony. Collaboration, Reputation and
Recommender Systems in Social Web Search, in F. Ricci, L. Rokach and B. Shapira,
Recommender Systems Handbook, Springer US, 2015: 569-608.

55. Problems and Issues
p Cold Start (new user and new item) - old items
are less interesting
p Learning to interact
p Measuring sys. performance
p Filter Bubble
p How much to personalize
p When to contextualize
p How to deliver
contextualized content?
p Multiple devices (synchronization)
p Short term (search) vs. long term (discovery)
personalization
55

56. Wrap up
p Search is not only lookup – we must develop
learn and investigate tools
p Queries are disappearing
p Good results necessitate personalization –
tourism is still behind other industries
p Preference learning requires new GUIs and
methods
p Context and proactivity is a mandatory dimension
p We are social creatures – we need group-adapted
tools.
56