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.

1. Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol

2. Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol
2015

3. Understanding the Users

4. Understanding the Users
explicit user
data
structured

5. Understanding the Users
semi-structured
explicit user
data
user generated
content
structured

6. Understanding the Users
semi-structured
implicit user
interactions
explicit user
data
user generated
content
structured un-structured

7. Understanding the Users
structured un-structured
semi-structured

8. Understanding the Users
structured
un-structured
semi-structured
explicit feedback
optimal data, but very rare
(limited in applications/items/attributes)

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. 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. explicit, clear and structuredstructured
understanding the users from explicit feedback

12. explicit, clear and structuredstructured
understanding the users from explicit feedback

13. explicit, clear and structuredstructured
understanding the users from explicit feedback

14. explicit, clear and structuredstructured
understanding the users from explicit feedback

15. explicit, clear and structuredstructured
understanding the users from explicit feedback

16. implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback

17. implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
navigational patterns
user behavior

18. implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
navigational patterns
user behavior
item importance
content recommendation

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. user generated content
understanding the users from their content
semi-structured

21. user generated content
“ the users are always
leaving information behind them ”
understanding the users from their content
semi-structured

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. “ the users are always
leaving information behind them ”
semi-structured
Understanding the users from their content
beyond the scope of their action

24. “ the users are always
leaving information behind them ”
semi-structured
Understanding the users from their content
beyond the scope of their action

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. “connecting people with great local businesses”

27. 5 stars rating explicit information
clear and easy to
understand
“connecting people with great local businesses”

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. Understand User’s Taste

30. identify the “food words” inside the review
understand the user’s opinion
Understand User’s Taste

31. identify the “food words” inside the review
understand the user’s opinion
Understand User’s Taste
build a user taste profile

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

34. user taste
profile
restaurant
kitchen quality
profile

35. user taste
profile
restaurant
kitchen quality
profile
user visits
a new place

36. user taste
profile
restaurant
kitchen quality
profile
user visits
a new place

37. user taste
profile
restaurant
kitchen quality
profile
user visits
a new place
food or menu
recommendation
“Buon Appetito - Recommending Personalized Menus”, HT’14

38. 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

39. How to do it?

40. food
words
sentiment
How to do it?
“Comparing and Combining Sentiment Analysis Methods”, CONS’13

41. food
words
sentiment
WOW +1lamb
How to do it?
“Comparing and Combining Sentiment Analysis Methods”, CONS’13

42. food
words
sentiment
delicious +1
WOW +1
dauphinoise
potatoes
lamb
How to do it?
“Comparing and Combining Sentiment Analysis Methods”, CONS’13

43. Recommendation Experiment.
Food/Menu Recommender

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. 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

46. User Implicit Feedback
semi-structuredstructured un-structured

47. User Implicit Feedback
un-structured

48. User Interactions

49. User Interactions

50. User Interactions
user’s session — what we know about the user’s behavior

51. User Interactions
user’s session — what we know about the user’s behavior
external referrer URL — where the user is coming from

52. User Browsing Graph

53. User Browsing Graph

54. User Browsing Graph
user session

55. User Browsing Graph

56. 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

57. User Browsing Graph

58. User Browsing Graph

59. 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)

60. User Browsing Graph
“Discovering Social Photo Navigation Patterns”, ICME’12
identifying from where
users are entering the
website
(external) referrer URL

61. 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?

62. User Browsing Graph
Does the referrer URL tell us something about the user’s session?

63. User Browsing Graph
Does the referrer URL tell us something about the user’s session?

64. 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?

65. 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, …)

66. 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

67. The Predictive Power
of the Referrer Domain

68. The Predictive Power
of the Referrer Domain

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

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

71. The Predictive Power
of the Referrer Domain

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

73. 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 ?

74. User Browsing Graph

75. User Browsing Graph
2 months of logs
~300M page views
~40M user sessions
~10M unique users
Flickr Data

76. User Browsing Graph
2 months of logs
~300M page views
~40M user sessions
~10M unique users
Flickr Data
considering
only photo
web page

77. 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

78. Photo Ranking
Through the Browse Graph

79. centrality based
approaches
Photo Ranking
Through the Browse Graph

80. centrality based
approaches
Photo Ranking
Through the Browse Graph
PageRank
BrowseRank

81. centrality based
approaches
Photo Ranking
Through the Browse Graph
PageRank
BrowseRank
standard approaches
explicit + stats

82. centrality based
approaches
Photo Ranking
Through the Browse Graph
PageRank
BrowseRank
standard approaches
explicit + stats
Favorites
Views
View Time

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

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

85. Photo Ranking
Through the Browse Graph
Evaluation

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

87. Photo Ranking
Through the Browse Graph
Evaluation
internal popularity — how popular is the photo within Flickr?
collective attention — implicit visibility of the photo

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?

89. 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?

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. Photo Ranking
Through the Browse Graph
Internal Popularity
x-axis: top N results ([1,1000] images)
y-axis: cumulative value of the features
legend

92. 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

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. Photo Ranking
Through the Browse Graph
Collective Attention
x-axis: top N results ([1,1000] images)
y-axis: cumulative value of the features
legend

95. 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

96. Photo Ranking
Through the Browse Graph
External Popularity

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. 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. Photo Ranking
Through the Browse Graph
External Popularity
x-axis: top N results ([1,1000] images)
y-axis: cumulative value of the features
legend

100. 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?

101. Photo Ranking
Through the Browse Graph
Diversity

102. Photo Ranking
Through the Browse Graph
Diversity
View and Time : rank
better tagged photos

103. Photo Ranking
Through the Browse Graph
Diversity
View and Time : rank
better tagged photos
BR and PG : rank
better photos that
have more tags

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. 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

106. 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

107. Recap
Analysis of the Browsing Logs

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

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. 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

111. 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?

112. Can we predict the content
the user is going to consume?

113. Can we predict the content
the user is going to consume?
un-structured

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

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

116. Can we predict the content
the user is going to consume?
un-structured
implicit information
(navigational patterns)
prediction / recommendation
browsing graph
(referrer graph)

117. Browse Graph on News
Predicting News Articles Consumption

118. Browse Graph on News
Predicting News Articles Consumption
Different Context : News Website

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]

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

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]
[3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010

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
[3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010

123. [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

124. Browse Graph on News
Predicting News Articles Consumption

125. Browse Graph on News
Predicting News Articles Consumption
Yahoo News
BrowseGraph
~500M pageviews

126. Browse Graph on News
Predicting News Articles Consumption
Yahoo News
BrowseGraph
~500M pageviews
Social Network Search Engine

127. Browse Graph on News
Predicting News Articles Consumption
Yahoo News
BrowseGraph
~500M pageviews
Social Network Search Engine

128. “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.

129. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are
differentiable by the referrer domains

130. 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

131. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph

132. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of hops
during browsing sessions

133. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of hops
during browsing sessions

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

135. 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

136. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importance

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. 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

139. 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
⌧

140. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories

141. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories

142. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories

143. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories

144. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph

145. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are
differentiable by the referrer domains

146. 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)

147. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph

148. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
first
news page

149. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
first
news page

150. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
first
news page

151. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection
• random [rnd]
random
first
news page

152. 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

153. 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

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
first
news page

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 [edge]
first
news page

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
CB
• content-based [cb]
(TF-IDF + similarity)

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)

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]

159. 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]
+

160. Precision @1
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)

161. Precision @1
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)

162. MRR @3
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)

163. MRR @3
Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)

164. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph

165. Browse Graph on News
Predicting News Articles ConsumptionYahoo News
BrowseGraph
About the ReferrerGraph :

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

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

168. 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

169. Recap
User Interaction

170. users
browsing traces
Recap
User Interaction

171. users
browsing traces
BrowseGraph
Recap
User Interaction

172. users
browsing traces
BrowseGraph
Recap
User Interaction

173. users
browsing traces
BrowseGraph
prediction
and
personalization
Recap
User Interaction

174. User interactions
Future Work

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, …)

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?)

177. 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, …)

178. Understanding the Users

179. un-structured
Understanding the Users
semi-structured
structured

180. un-structured
capture insights from
user’s content
Understanding the Users
semi-structured
structured

181. un-structured
capture insights from
user’s content
capture insights from
user’s interaction
Understanding the Users
semi-structured
structured

182. Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol
Questions?
micheletrevisiol.com
un-sem
st

183. Capturing Insights
from Users Actions
Beyond Explicit Information
michele trevisiol
2015
Questions?
micheletrevisiol.com
un-sem
st