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Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol
Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol
2015
Understanding the Users
Understanding the Users
explicit user
data
structured
Understanding the Users
semi-structured
explicit user
data
user generated
content
structured
Understanding the Users
semi-structured
implicit user
interactions
explicit user
data
user generated
content
structured un...
Understanding the Users
structured un-structured
semi-structured
Understanding the Users
structured
un-structured
semi-structured
explicit feedback

optimal data, but very rare 

(limited...
Understanding the Users
structured
un-structured
semi-structured
user generated content
good data, common and with lot of
...
Understanding the Users
structured
un-structured
semi-structured
implicit data
tough data, but very common

(it is often h...
explicit, clear and structuredstructured
understanding the users from explicit feedback
explicit, clear and structuredstructured
understanding the users from explicit feedback
explicit, clear and structuredstructured
understanding the users from explicit feedback
explicit, clear and structuredstructured
understanding the users from explicit feedback
explicit, clear and structuredstructured
understanding the users from explicit feedback
implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
navigational patterns
user be...
implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
navigational patterns
user be...
implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
navigational patterns
user be...
user generated content
understanding the users from their content
semi-structured
user generated content
“ the users are always
leaving information behind them ”
understanding the users from their content...
user generated content
reviews / opinions
comments
media 

(images / visual content)
meta-data (gps, tags, ..)
interests +...
“ the users are always
leaving information behind them ”
semi-structured
Understanding the users from their content

beyon...
“ the users are always
leaving information behind them ”
semi-structured
Understanding the users from their content

beyon...
“ the users are always
leaving
Loud and Trendy: Crowdsourcing
Impressions of Social Ambiance in Popular
Indoor Urban Place...
“connecting people with great local businesses”
5 stars rating explicit information
clear and easy to
understand
“connecting people with great local businesses”
5 stars rating explicit information
clear and easy to
understand
unstructured and noisy
contains extremely meaningful info...
Understand User’s Taste
identify the “food words” inside the review
understand the user’s opinion
Understand User’s Taste
identify the “food words” inside the review
understand the user’s opinion
Understand User’s Taste
build a user taste profile
identify the “food words” inside the review
understand the user’s opinion
Understand User’s Taste
build a user taste profil...
user taste
profile
restaurant
kitchen quality
profile
user taste
profile
restaurant
kitchen quality
profile
user visits
a new place
user taste
profile
restaurant
kitchen quality
profile
user visits
a new place
user taste
profile
restaurant
kitchen quality
profile
user visits
a new place
food or menu
recommendation
“Buon Appetito - R...
user taste
profile
restaurant
kitchen quality
profile
user visits
a new place
what the user likes
the “specialities” of the
...
How to do it?
food
words
sentiment
How to do it?
“Comparing and Combining Sentiment Analysis Methods”, CONS’13
food
words
sentiment
WOW +1lamb
How to do it?
“Comparing and Combining Sentiment Analysis Methods”, CONS’13
food
words
sentiment
delicious +1
WOW +1
dauphinoise
potatoes
lamb
How to do it?
“Comparing and Combining Sentiment Analys...
Recommendation Experiment.
Food/Menu Recommender
Recommendation Experiment.
[avg-sent] 

most frequent positive food items among the profiles (> threshold)
[user-words] 

u...
Recommendation Experiment.
[avg-sent] 

most frequent positive food items among the profiles (> threshold)
[user-words] 

u...
User Implicit Feedback
semi-structuredstructured un-structured
User Implicit Feedback
un-structured
User Interactions
User Interactions
User Interactions
user’s session — what we know about the user’s behavior
User Interactions
user’s session — what we know about the user’s behavior
external referrer URL — where the user is coming...
User Browsing Graph
User Browsing Graph
User Browsing Graph
user session
User Browsing Graph
User Browsing Graph
collect all browsing sessions
BrowseGraph
(wighted graph)
“BrowseRank: letting web users vote for page...
User Browsing Graph
User Browsing Graph
User Browsing Graph
“Discovering Social Photo Navigation Patterns”, ICME’12
identifying from where
users are entering the
...
User Browsing Graph
“Discovering Social Photo Navigation Patterns”, ICME’12
identifying from where
users are entering the
...
User Browsing Graph
“Discovering Social Photo Navigation Patterns”, ICME’12
identifying from where
users are entering the
...
User Browsing Graph
Does the referrer URL tell us something about the user’s session?
User Browsing Graph
Does the referrer URL tell us something about the user’s session?
User Browsing Graph
mail
search
engine
blogs
social
network
labeling referrer URLs (top domains)
Does the referrer URL tel...
User Browsing Graph
mail
search
engine
blogs
social
network
labeling referrer URLs (top domains)
“Discovering Social Photo...
The Predictive Power 

of the Referrer Domain
sample of 2 months
of Flickr logs
Apache Web Logs
<user_id,	
  timestamp,	
 ...
The Predictive Power 

of the Referrer Domain
The Predictive Power 

of the Referrer Domain
The Predictive Power 

of the Referrer Domain
From Search Engines:
• standard + advanced search (CC)
The Predictive Power 

of the Referrer Domain
From Search Engines:
• standard + advanced search (CC)
From Mail:
• manage f...
The Predictive Power 

of the Referrer Domain
Visitors behave differently depending on where they
come from
Users tend to perform similar sessions when coming
from the ...
Visitors behave differently depending on where they
come from
Users tend to perform similar sessions when coming
from the ...
User Browsing Graph
User Browsing Graph
2 months of logs
~300M page views
~40M user sessions
~10M unique users
Flickr Data
User Browsing Graph
2 months of logs
~300M page views
~40M user sessions
~10M unique users
Flickr Data
considering
only ph...
User Browsing Graph
BrowseGraph
(wighted graph)
ranking of photos based
on browsing behavior
“Image Ranking Based on Users...
Photo Ranking 

Through the Browse Graph
centrality based
approaches
Photo Ranking 

Through the Browse Graph
centrality based
approaches
Photo Ranking 

Through the Browse Graph
PageRank
BrowseRank
centrality based
approaches
Photo Ranking 

Through the Browse Graph
PageRank
BrowseRank
standard approaches

explicit + s...
centrality based
approaches
Photo Ranking 

Through the Browse Graph
PageRank
BrowseRank
standard approaches

explicit + s...
Photo Ranking 

Through the Browse Graph
PageRank BrowseRank Favorites Views TimeSpent
Photo Ranking 

Through the Browse Graph
PageRank BrowseRank Favorites Views TimeSpent
.
.
.
.
.
.
.
.
.
.
Photo Ranking 

Through the Browse Graph
Evaluation
Photo Ranking 

Through the Browse Graph
Evaluation
internal popularity — how popular is the photo within Flickr?
Photo Ranking 

Through the Browse Graph
Evaluation
internal popularity — how popular is the photo within Flickr?
collecti...
Photo Ranking 

Through the Browse Graph
Evaluation
internal popularity — how popular is the photo within Flickr?
collecti...
Photo Ranking 

Through the Browse Graph
Evaluation
internal popularity — how popular is the photo within Flickr?
collecti...
Photo Ranking 

Through the Browse Graph
Internal Popularity
x-axis: top N results ([1,1000] images)

y-axis: cumulative v...
Photo Ranking 

Through the Browse Graph
Internal Popularity
x-axis: top N results ([1,1000] images)

y-axis: cumulative v...
Photo Ranking 

Through the Browse Graph
Internal Popularity
x-axis: top N results ([1,1000] images)

y-axis: cumulative v...
Photo Ranking 

Through the Browse Graph
Collective Attention
x-axis: top N results ([1,1000] images)

y-axis: cumulative ...
Photo Ranking 

Through the Browse Graph
Collective Attention
x-axis: top N results ([1,1000] images)

y-axis: cumulative ...
Photo Ranking 

Through the Browse Graph
Collective Attention
x-axis: top N results ([1,1000] images)

y-axis: cumulative ...
Photo Ranking 

Through the Browse Graph
External Popularity
Photo Ranking 

Through the Browse Graph
External Popularity
x-axis: top N results ([1,1000] images)

y-axis: cumulative v...
Photo Ranking 

Through the Browse Graph
External Popularity
x-axis: top N results ([1,1000] images)

y-axis: cumulative v...
Photo Ranking 

Through the Browse Graph
External Popularity
x-axis: top N results ([1,1000] images)

y-axis: cumulative v...
Photo Ranking 

Through the Browse Graph
External Popularity
x-axis: top N results ([1,1000] images)

y-axis: cumulative v...
Photo Ranking 

Through the Browse Graph
Diversity
Photo Ranking 

Through the Browse Graph
Diversity
View and Time : rank 

better tagged photos
Photo Ranking 

Through the Browse Graph
Diversity
View and Time : rank 

better tagged photos
BR and PG : rank 

better p...
Photo Ranking 

Through the Browse Graph
Visual Inspection ~October
Artistic Events Series Peculiar
BR 4 3 1 2
PR 4 3 2 1
...
Photo Ranking 

Through the Browse Graph
Visual Inspection ~October
Artistic Events Series Peculiar
BR 4 3 1 2
PR 4 3 2 1
...
Photo Ranking 

Through the Browse Graph
Visual Inspection ~October
Artistic Events Series Peculiar
BR 4 3 1 2
PR 4 3 2 1
...
Recap
Analysis of the Browsing Logs
About the Referrer URL :
information about the session the user is going to do
understanding how the webpages are linked f...
About the Referrer URL :
information about the session the user is going to do
understanding how the webpages are linked f...
About the Referrer URL :
information about the session the user is going to do
understanding how the webpages are linked f...
About the Referrer URL :
information about the session the user is going to do
understanding how the webpages are linked f...
Can we predict the content
the user is going to consume?
Can we predict the content
the user is going to consume?
un-structured
Can we predict the content
the user is going to consume?
un-structured
implicit information 

(navigational patterns)
Can we predict the content
the user is going to consume?
un-structured
implicit information 

(navigational patterns)
brow...
Can we predict the content
the user is going to consume?
un-structured
implicit information 

(navigational patterns)
pred...
Browse Graph on News
Predicting News Articles Consumption
Browse Graph on News
Predicting News Articles Consumption
Different Context : News Website
[1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google
[2] www.people-press....
[1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google
[2] www.people-press....
[1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google
[2] www.people-press....
[1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google
[2] www.people-press....
[1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google
[2] www.people-press....
Browse Graph on News
Predicting News Articles Consumption
Browse Graph on News
Predicting News Articles Consumption
Yahoo News
BrowseGraph
~500M pageviews
Browse Graph on News
Predicting News Articles Consumption
Yahoo News
BrowseGraph
~500M pageviews
Social Network Search Eng...
Browse Graph on News
Predicting News Articles Consumption
Yahoo News
BrowseGraph
~500M pageviews
Social Network Search Eng...
“Cold-start News Recommendation with Domain-dependent Browse Graph”, RecSys’14
Browse Graph on News
Predicting News Articl...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are
di...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are
di...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of ...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of ...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of ...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of ...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importance
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importance
homepage
goog...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importance
homepage
goog...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importance
homepage
goog...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are
di...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are
di...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
first
news page
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
first
news page
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
first...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node...
Precision @1
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly gr...
Precision @1
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly gr...
MRR @3
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (...
MRR @3
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
About the ReferrerGraph :
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
About the ReferrerGraph :
prediction infor...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
About the ReferrerGraph :
prediction infor...
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
About the ReferrerGraph :
prediction infor...
Recap
User Interaction
users 

browsing traces
Recap
User Interaction
users 

browsing traces
BrowseGraph
Recap
User Interaction
users 

browsing traces
BrowseGraph
Recap
User Interaction
users 

browsing traces
BrowseGraph
prediction

and

personalization
Recap
User Interaction
User interactions
Future Work
User interactions
Future Work
Extending Implicit Signals
location data (IP Address, Mobile GPS)
device type (tablet vs. mo...
User interactions
Future Work
Extending Implicit Signals
location data (IP Address, Mobile GPS)
device type (tablet vs. mo...
User interactions
Future Work
Extending Implicit Signals
location data (IP Address, Mobile GPS)
device type (tablet vs. mo...
Understanding the Users
un-structured
Understanding the Users
semi-structured
structured
un-structured
capture insights from
user’s content
Understanding the Users
semi-structured
structured
un-structured
capture insights from
user’s content
capture insights from
user’s interaction
Understanding the Users
semi-s...
Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol
Questions?
micheletrevisiol.com
un-sem...
Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol
2015
Questions?
micheletrevisiol.com
u...
Keynote @iSWAG2015
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Keynote @iSWAG2015

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Measuring the volume of information that the users, deliberately or not, leave on-line is an impossible mission. The vast majority of the actions performed by the users enclose more information than what the users themselves think they are producing.

To shed light on this truth, the talk will start with different examples of implicit information, namely traces that are often hidden inside other explicit feedbacks, or directly detectable by the users' actions. The focus will then move to browsing behavior analysis, including approaches to get a deeper understanding of the users, in particular cold-start situations. The talk will conclude showing how to follow these users traces to obtain reliable knowledge about the content consumed by the end-users.

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Keynote @iSWAG2015

  1. 1. Capturing Insights from Users Actions Beyond Explicit Information michele trevisiol
  2. 2. Capturing Insights from Users Actions Beyond Explicit Information michele trevisiol 2015
  3. 3. Understanding the Users
  4. 4. Understanding the Users explicit user data structured
  5. 5. Understanding the Users semi-structured explicit user data user generated content structured
  6. 6. Understanding the Users semi-structured implicit user interactions explicit user data user generated content structured un-structured
  7. 7. Understanding the Users structured un-structured semi-structured
  8. 8. Understanding the Users structured un-structured semi-structured explicit feedback
 optimal data, but very rare 
 (limited in applications/items/attributes)
  9. 9. Understanding the Users structured un-structured semi-structured user generated content good data, common and with lot of knowledge, but often difficult to use explicit feedback
 optimal data, but very rare 
 (limited in applications/items/attributes)
  10. 10. Understanding the Users structured un-structured semi-structured implicit data tough data, but very common
 (it is often hard to understand) user generated content good data, common and with lot of knowledge, but often difficult to use explicit feedback
 optimal data, but very rare 
 (limited in applications/items/attributes)
  11. 11. explicit, clear and structuredstructured understanding the users from explicit feedback
  12. 12. explicit, clear and structuredstructured understanding the users from explicit feedback
  13. 13. explicit, clear and structuredstructured understanding the users from explicit feedback
  14. 14. explicit, clear and structuredstructured understanding the users from explicit feedback
  15. 15. explicit, clear and structuredstructured understanding the users from explicit feedback
  16. 16. implicit, noisy and unstructuredun-structured understanding the users from implicit feedback
  17. 17. implicit, noisy and unstructuredun-structured understanding the users from implicit feedback navigational patterns user behavior
  18. 18. implicit, noisy and unstructuredun-structured understanding the users from implicit feedback navigational patterns user behavior item importance content recommendation
  19. 19. implicit, noisy and unstructuredun-structured understanding the users from implicit feedback navigational patterns user behavior item importance content recommendation browsing graph referrer graph
  20. 20. user generated content understanding the users from their content semi-structured
  21. 21. user generated content “ the users are always leaving information behind them ” understanding the users from their content semi-structured
  22. 22. user generated content reviews / opinions comments media 
 (images / visual content) meta-data (gps, tags, ..) interests + social tweets / vine videos “ the users are always leaving information behind them ” understanding the users from their content semi-structured
  23. 23. “ the users are always leaving information behind them ” semi-structured Understanding the users from their content
 beyond the scope of their action
  24. 24. “ the users are always leaving information behind them ” semi-structured Understanding the users from their content
 beyond the scope of their action
  25. 25. “ the users are always leaving Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places, CH’15 semi-structured Understanding the users from their content
 beyond the scope of their action
  26. 26. “connecting people with great local businesses”
  27. 27. 5 stars rating explicit information clear and easy to understand “connecting people with great local businesses”
  28. 28. 5 stars rating explicit information clear and easy to understand unstructured and noisy contains extremely meaningful information “connecting people with great local businesses”
  29. 29. Understand User’s Taste
  30. 30. identify the “food words” inside the review understand the user’s opinion Understand User’s Taste
  31. 31. identify the “food words” inside the review understand the user’s opinion Understand User’s Taste build a user taste profile
  32. 32. identify the “food words” inside the review understand the user’s opinion Understand User’s Taste build a user taste profile build a restaurant 
 “kitchen quality” profile
  33. 33. user taste profile restaurant kitchen quality profile
  34. 34. user taste profile restaurant kitchen quality profile user visits a new place
  35. 35. user taste profile restaurant kitchen quality profile user visits a new place
  36. 36. user taste profile restaurant kitchen quality profile user visits a new place food or menu recommendation “Buon Appetito - Recommending Personalized Menus”, HT’14
  37. 37. user taste profile restaurant kitchen quality profile user visits a new place what the user likes the “specialities” of the restaurant: serendipity? food or menu recommendation “Buon Appetito - Recommending Personalized Menus”, HT’14
  38. 38. How to do it?
  39. 39. food words sentiment How to do it? “Comparing and Combining Sentiment Analysis Methods”, CONS’13
  40. 40. food words sentiment WOW +1lamb How to do it? “Comparing and Combining Sentiment Analysis Methods”, CONS’13
  41. 41. food words sentiment delicious +1 WOW +1 dauphinoise potatoes lamb How to do it? “Comparing and Combining Sentiment Analysis Methods”, CONS’13
  42. 42. Recommendation Experiment. Food/Menu Recommender
  43. 43. Recommendation Experiment. [avg-sent] 
 most frequent positive food items among the profiles (> threshold) [user-words] 
 user-based CF with weighted items by positive sentiments [menu-words] 
 frequent and good menu/item sets (Fuzzy Apriori) [zero-sent] 
 most frequent food items among the profiles (no sentiments) Food/Menu Recommender
  44. 44. Recommendation Experiment. [avg-sent] 
 most frequent positive food items among the profiles (> threshold) [user-words] 
 user-based CF with weighted items by positive sentiments [menu-words] 
 frequent and good menu/item sets (Fuzzy Apriori) [zero-sent] 
 most frequent food items among the profiles (no sentiments) Food/Menu Recommender
  45. 45. User Implicit Feedback semi-structuredstructured un-structured
  46. 46. User Implicit Feedback un-structured
  47. 47. User Interactions
  48. 48. User Interactions
  49. 49. User Interactions user’s session — what we know about the user’s behavior
  50. 50. User Interactions user’s session — what we know about the user’s behavior external referrer URL — where the user is coming from
  51. 51. User Browsing Graph
  52. 52. User Browsing Graph
  53. 53. User Browsing Graph user session
  54. 54. User Browsing Graph
  55. 55. User Browsing Graph collect all browsing sessions BrowseGraph (wighted graph) “BrowseRank: letting web users vote for page importance”, SIGIR’08 “Image Ranking Based on Users Browsing Behavior”, SIGIR’12
  56. 56. User Browsing Graph
  57. 57. User Browsing Graph
  58. 58. User Browsing Graph “Discovering Social Photo Navigation Patterns”, ICME’12 identifying from where users are entering the website capture users’ interest (collecting user’s browsing patterns)
  59. 59. User Browsing Graph “Discovering Social Photo Navigation Patterns”, ICME’12 identifying from where users are entering the website (external) referrer URL
  60. 60. User Browsing Graph “Discovering Social Photo Navigation Patterns”, ICME’12 identifying from where users are entering the website (external) referrer URL Does the referrer URL tell us something about the user’s session?
  61. 61. User Browsing Graph Does the referrer URL tell us something about the user’s session?
  62. 62. User Browsing Graph Does the referrer URL tell us something about the user’s session?
  63. 63. User Browsing Graph mail search engine blogs social network labeling referrer URLs (top domains) Does the referrer URL tell us something about the user’s session?
  64. 64. User Browsing Graph mail search engine blogs social network labeling referrer URLs (top domains) “Discovering Social Photo Navigation Patterns”, ICME’12 Does the referrer URL tell us something about the user’s session? classify Flickr web pages (photos, groups, profile, …)
  65. 65. The Predictive Power 
 of the Referrer Domain sample of 2 months of Flickr logs Apache Web Logs <user_id,  timestamp,  referrer_url,  current_url,  user_agent> ~300M page views ~40M user sessions ~10M unique users Flickr Data
  66. 66. The Predictive Power 
 of the Referrer Domain
  67. 67. The Predictive Power 
 of the Referrer Domain
  68. 68. The Predictive Power 
 of the Referrer Domain From Search Engines: • standard + advanced search (CC)
  69. 69. The Predictive Power 
 of the Referrer Domain From Search Engines: • standard + advanced search (CC) From Mail: • manage friends
  70. 70. The Predictive Power 
 of the Referrer Domain
  71. 71. Visitors behave differently depending on where they come from Users tend to perform similar sessions when coming from the same referrer class (domain) Note: referrer URL comes for free! The Predictive Power 
 of the Referrer Domain “Discovering Social Photo Navigation Patterns”, ICME’12
  72. 72. Visitors behave differently depending on where they come from Users tend to perform similar sessions when coming from the same referrer class (domain) Note: referrer URL comes for free! The Predictive Power 
 of the Referrer Domain “Discovering Social Photo Navigation Patterns”, ICME’12 What kind of knowledge the referrer URL adds within the BrowseGraph ?
  73. 73. User Browsing Graph
  74. 74. User Browsing Graph 2 months of logs ~300M page views ~40M user sessions ~10M unique users Flickr Data
  75. 75. User Browsing Graph 2 months of logs ~300M page views ~40M user sessions ~10M unique users Flickr Data considering only photo web page
  76. 76. User Browsing Graph BrowseGraph (wighted graph) ranking of photos based on browsing behavior “Image Ranking Based on Users Browsing Behavior”, SIGIR’12 2 months of logs ~300M page views ~40M user sessions ~10M unique users Flickr Data considering only photo web page
  77. 77. Photo Ranking 
 Through the Browse Graph
  78. 78. centrality based approaches Photo Ranking 
 Through the Browse Graph
  79. 79. centrality based approaches Photo Ranking 
 Through the Browse Graph PageRank BrowseRank
  80. 80. centrality based approaches Photo Ranking 
 Through the Browse Graph PageRank BrowseRank standard approaches
 explicit + stats
  81. 81. centrality based approaches Photo Ranking 
 Through the Browse Graph PageRank BrowseRank standard approaches
 explicit + stats Favorites Views View Time
  82. 82. Photo Ranking 
 Through the Browse Graph PageRank BrowseRank Favorites Views TimeSpent
  83. 83. Photo Ranking 
 Through the Browse Graph PageRank BrowseRank Favorites Views TimeSpent . . . . . . . . . .
  84. 84. Photo Ranking 
 Through the Browse Graph Evaluation
  85. 85. Photo Ranking 
 Through the Browse Graph Evaluation internal popularity — how popular is the photo within Flickr?
  86. 86. Photo Ranking 
 Through the Browse Graph Evaluation internal popularity — how popular is the photo within Flickr? collective attention — implicit visibility of the photo
  87. 87. Photo Ranking 
 Through the Browse Graph Evaluation internal popularity — how popular is the photo within Flickr? collective attention — implicit visibility of the photo external popularity — how popular is the photo outside Flickr?
  88. 88. Photo Ranking 
 Through the Browse Graph Evaluation internal popularity — how popular is the photo within Flickr? collective attention — implicit visibility of the photo external popularity — how popular is the photo outside Flickr? diversity — how diverse is the ranking?
  89. 89. Photo Ranking 
 Through the Browse Graph Internal Popularity x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend
  90. 90. Photo Ranking 
 Through the Browse Graph Internal Popularity x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend
  91. 91. Photo Ranking 
 Through the Browse Graph Internal Popularity x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features Favorites : ranks images with highest internal engagement legend
  92. 92. Photo Ranking 
 Through the Browse Graph Collective Attention x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend
  93. 93. Photo Ranking 
 Through the Browse Graph Collective Attention x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend
  94. 94. Photo Ranking 
 Through the Browse Graph Collective Attention x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend Favorites and Views are not very correlated
  95. 95. Photo Ranking 
 Through the Browse Graph External Popularity
  96. 96. Photo Ranking 
 Through the Browse Graph External Popularity x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend
  97. 97. Photo Ranking 
 Through the Browse Graph External Popularity x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend
  98. 98. Photo Ranking 
 Through the Browse Graph External Popularity x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features legend
  99. 99. Photo Ranking 
 Through the Browse Graph External Popularity x-axis: top N results ([1,1000] images)
 y-axis: cumulative value of the features Favorites : low correlation with external visibility Page/BrowseRank : very high correlation —thanks to the referrer?
  100. 100. Photo Ranking 
 Through the Browse Graph Diversity
  101. 101. Photo Ranking 
 Through the Browse Graph Diversity View and Time : rank 
 better tagged photos
  102. 102. Photo Ranking 
 Through the Browse Graph Diversity View and Time : rank 
 better tagged photos BR and PG : rank 
 better photos that 
 have more tags
  103. 103. Photo Ranking 
 Through the Browse Graph Visual Inspection ~October Artistic Events Series Peculiar BR 4 3 1 2 PR 4 3 2 1 Favorites 4 2 4 0 Views 2 1 7 0 View Time 1 2 7 0
  104. 104. Photo Ranking 
 Through the Browse Graph Visual Inspection ~October Artistic Events Series Peculiar BR 4 3 1 2 PR 4 3 2 1 Favorites 4 2 4 0 Views 2 1 7 0 View Time 1 2 7 0
  105. 105. Photo Ranking 
 Through the Browse Graph Visual Inspection ~October Artistic Events Series Peculiar BR 4 3 1 2 PR 4 3 2 1 Favorites 4 2 4 0 Views 2 1 7 0 View Time 1 2 7 0 Artistic Events Series Peculiar BR 4 3 1 2 PR 4 3 2 1 Favorites 4 2 4 0 Views 2 1 7 0 View Time 1 2 7 0 Series : they capture temporary interest of few communities very well connected inside Flickr
  106. 106. Recap Analysis of the Browsing Logs
  107. 107. About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world Recap Analysis of the Browsing Logs
  108. 108. About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world Recap Analysis of the Browsing Logs About the BrowseGraph : discovering content “voted” by the users extending the informativeness with the Referrer URL
  109. 109. About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world Recap Analysis of the Browsing Logs About the BrowseGraph : discovering content “voted” by the users extending the informativeness with the Referrer URL
  110. 110. About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world Recap Analysis of the Browsing Logs About the BrowseGraph : discovering content “voted” by the users extending the informativeness with the Referrer URL Can we predict the content the user is going to consume?
  111. 111. Can we predict the content the user is going to consume?
  112. 112. Can we predict the content the user is going to consume? un-structured
  113. 113. Can we predict the content the user is going to consume? un-structured implicit information 
 (navigational patterns)
  114. 114. Can we predict the content the user is going to consume? un-structured implicit information 
 (navigational patterns) browsing graph
 (referrer graph)
  115. 115. Can we predict the content the user is going to consume? un-structured implicit information 
 (navigational patterns) prediction / recommendation browsing graph
 (referrer graph)
  116. 116. Browse Graph on News Predicting News Articles Consumption
  117. 117. Browse Graph on News Predicting News Articles Consumption Different Context : News Website
  118. 118. [1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google [2] www.people-press.org/2012/09/27/section-2-online-and-digital-news-2 Browse Graph on News Predicting News Articles Consumption Different Context : News Website major portion of the overall Web traffic [1,2]
  119. 119. [1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google [2] www.people-press.org/2012/09/27/section-2-online-and-digital-news-2 Browse Graph on News Predicting News Articles Consumption Different Context : News Website major portion of the overall Web traffic [1,2] thousands of news articles per day
  120. 120. [1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google [2] www.people-press.org/2012/09/27/section-2-online-and-digital-news-2 Browse Graph on News Predicting News Articles Consumption Different Context : News Website major portion of the overall Web traffic [1,2] thousands of news articles per day very short sessions [3] [3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010
  121. 121. [1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google [2] www.people-press.org/2012/09/27/section-2-online-and-digital-news-2 Browse Graph on News Predicting News Articles Consumption Different Context : News Website major portion of the overall Web traffic [1,2] thousands of news articles per day very short sessions [3] many visitors are newcomers [3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010
  122. 122. [1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google [2] www.people-press.org/2012/09/27/section-2-online-and-digital-news-2 Browse Graph on News Predicting News Articles Consumption Different Context : News Website major portion of the overall Web traffic [1,2] thousands of news articles per day very short sessions [3] many visitors are newcomers links to news articles shared around the Web [3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010
  123. 123. Browse Graph on News Predicting News Articles Consumption
  124. 124. Browse Graph on News Predicting News Articles Consumption Yahoo News BrowseGraph ~500M pageviews
  125. 125. Browse Graph on News Predicting News Articles Consumption Yahoo News BrowseGraph ~500M pageviews Social Network Search Engine
  126. 126. Browse Graph on News Predicting News Articles Consumption Yahoo News BrowseGraph ~500M pageviews Social Network Search Engine
  127. 127. “Cold-start News Recommendation with Domain-dependent Browse Graph”, RecSys’14 Browse Graph on News Predicting News Articles Consumption Yahoo News BrowseGraph ~500M pageviews Social Network Search Engine Domain-Dependent BrowseGraph ..or just referrerGraph.
  128. 128. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph hypothesis : news articles consumed are differentiable by the referrer domains
  129. 129. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph hypothesis : news articles consumed are differentiable by the referrer domains implement and evaluate a 
 recommender system based on the referrerGraphs
  130. 130. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph
  131. 131. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph sessions are very short average number of hops 
 during browsing sessions
  132. 132. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph sessions are very short average number of hops 
 during browsing sessions
  133. 133. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph sessions are very short average number of hops 
 during browsing sessions very different size
  134. 134. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph sessions are very short average number of hops 
 during browsing sessions very different size well connected
  135. 135. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Nodes Overlap and Importance
  136. 136. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Nodes Overlap and Importance homepage google yahoo bing facebook twitter reddit homepage google yahoo bing facebook twitter reddit 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Jaccard Similarity of Node Sets
  137. 137. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Nodes Overlap and Importance homepage google yahoo bing facebook twitter reddit homepage google yahoo bing facebook twitter reddit 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Jaccard Similarity of Node Sets
  138. 138. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Nodes Overlap and Importance homepage google yahoo bing facebook twitter reddit homepage google yahoo bing facebook twitter reddit 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Jaccard Similarity of Node Sets homepage google yahoo bing facebook twitter reddit homepage google yahoo bing facebook twitter reddit 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Kendall Between News PageRanks ⌧
  139. 139. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Most Common Categories
  140. 140. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Most Common Categories
  141. 141. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Most Common Categories
  142. 142. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph Most Common Categories
  143. 143. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph
  144. 144. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph hypothesis : news articles consumed are differentiable by the referrer domains
  145. 145. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph hypothesis : news articles consumed are differentiable by the referrer domains different graph structure different interest of the users: individual articles (node) news articles topics importance (PageRank ranking)
  146. 146. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph
  147. 147. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph first news page
  148. 148. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph first news page
  149. 149. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] first news page
  150. 150. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] random first news page
  151. 151. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 first news page
  152. 152. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 popular • popular [pop] first news page
  153. 153. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 • popular [pop] 25 20 15 10 first news page
  154. 154. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 • popular [pop] 25 20 15 10 edge • edge [edge] first news page
  155. 155. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 • popular [pop] 25 20 15 10 • edge [edge] first news page CB • content-based [cb]
 (TF-IDF + similarity)
  156. 156. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 • popular [pop] 25 20 15 10 • edge [edge] first news page • content-based [cb]
 (TF-IDF + similarity)
  157. 157. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 • popular [pop] 25 20 15 10 • edge [edge] first news page • content-based [cb]
 (TF-IDF + similarity) • Full Graph [full]
  158. 158. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph graph selection • ReferrerGraph [ref] node selection • random [rnd] 40 30 60 80 • popular [pop] 25 20 15 10 • edge [edge] first news page • content-based [cb]
 (TF-IDF + similarity) • Full Graph [full] • Mix Approach [mix] +
  159. 159. Precision @1 Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph averaged over 1,438 hourly graphs (~350K per hour)
  160. 160. Precision @1 Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph averaged over 1,438 hourly graphs (~350K per hour)
  161. 161. MRR @3 Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph averaged over 1,438 hourly graphs (~350K per hour)
  162. 162. MRR @3 Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph averaged over 1,438 hourly graphs (~350K per hour)
  163. 163. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph
  164. 164. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph About the ReferrerGraph :
  165. 165. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph About the ReferrerGraph : prediction information of the referrer URL + 
 collective behaviors of the users
  166. 166. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph About the ReferrerGraph : prediction information of the referrer URL + 
 collective behaviors of the users able to capture interest of users —even for cold-start problem
  167. 167. Browse Graph on News Predicting News Articles ConsumptionYahoo News BrowseGraph About the ReferrerGraph : prediction information of the referrer URL + 
 collective behaviors of the users able to capture interest of users —even for cold-start problem extremely powerful in the news context
  168. 168. Recap User Interaction
  169. 169. users 
 browsing traces Recap User Interaction
  170. 170. users 
 browsing traces BrowseGraph Recap User Interaction
  171. 171. users 
 browsing traces BrowseGraph Recap User Interaction
  172. 172. users 
 browsing traces BrowseGraph prediction
 and
 personalization Recap User Interaction
  173. 173. User interactions Future Work
  174. 174. User interactions Future Work Extending Implicit Signals location data (IP Address, Mobile GPS) device type (tablet vs. mobile vs. desktop) custom webpage data (Social Media, …)
  175. 175. User interactions Future Work Extending Implicit Signals location data (IP Address, Mobile GPS) device type (tablet vs. mobile vs. desktop) custom webpage data (Social Media, …) Integrating User Profile long term user information user’s profile changes over time 
 (with respect to the referrer?)
  176. 176. User interactions Future Work Extending Implicit Signals location data (IP Address, Mobile GPS) device type (tablet vs. mobile vs. desktop) custom webpage data (Social Media, …) Integrating User Profile long term user information user’s profile changes over time 
 (with respect to the referrer?) Experiment Different Graphs graph of actions instead of pageviews? 
 (share actions, explicit activity, ads, …)
  177. 177. Understanding the Users
  178. 178. un-structured Understanding the Users semi-structured structured
  179. 179. un-structured capture insights from user’s content Understanding the Users semi-structured structured
  180. 180. un-structured capture insights from user’s content capture insights from user’s interaction Understanding the Users semi-structured structured
  181. 181. Capturing Insights from Users Actions Beyond Explicit Information michele trevisiol Questions? micheletrevisiol.com un-sem st
  182. 182. Capturing Insights from Users Actions Beyond Explicit Information michele trevisiol 2015 Questions? micheletrevisiol.com un-sem st

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