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Capturing Insights from Users ActionsBeyond Explicit Information
michele trevisiol
Capturing Insights from Users ActionsBeyond Explicit Information
michele trevisiol2015
Understanding the Users
Understanding the Users
explicit user data
structured
Understanding the Users
semi-structuredexplicit user
data
user generated content
structured
Understanding the Users
semi-structuredimplicit user interactions
explicit user data
user generated content
structured un-structured
Understanding the Users
structured un-structured
semi-structured
Understanding the Usersstructured
un-structured
semi-structured
explicit feedback optimal data, but very rare
(limited in applications/items/attributes)
Understanding the Usersstructured
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)
Understanding the Usersstructured
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)
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 patternsuser behavior
implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
navigational patternsuser behavior
item importancecontent recommendation
implicit, noisy and unstructuredun-structured
understanding the users from implicit feedback
navigational patternsuser behavior
item importancecontent recommendation
browsing graphreferrer graph
user generated contentunderstanding the users from their content
semi-structured
user generated content
“ the users are always leaving information behind them ”
understanding the users from their contentsemi-structured
user generated content
reviews / opinionscommentsmedia (images / visual content)meta-data (gps, tags, ..)interests + socialtweets / vine videos
“ the users are always leaving information behind them ”
understanding the users from their contentsemi-structured
“ the users are always leaving information behind them ”
semi-structuredUnderstanding the users from their content
beyond the scope of their action
“ the users are always leaving information behind them ”
semi-structuredUnderstanding the users from their content
beyond the scope of their action
“ the users are always leaving
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular
Indoor Urban Places, CH’15
semi-structuredUnderstanding the users from their content
beyond the scope of their action
“connecting people with great local businesses”
5 stars rating explicit informationclear and easy to understand
“connecting people with great local businesses”
5 stars rating explicit informationclear and easy to understand
unstructured and noisycontains extremely meaningful information
“connecting people with great local businesses”
Understand User’s Taste
identify the “food words” inside the reviewunderstand the user’s opinion
Understand User’s Taste
identify the “food words” inside the reviewunderstand the user’s opinion
Understand User’s Taste
build a user taste profile
identify the “food words” inside the reviewunderstand the user’s opinion
Understand User’s Taste
build a user taste profile build a restaurant “kitchen quality” profile
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 - Recommending Personalized Menus”, HT’14
user taste profile
restaurant kitchen quality
profile
user visits a new place
what the user likesthe “specialities” of the restaurant: serendipity?
food or menu recommendation
“Buon Appetito - Recommending Personalized Menus”, HT’14
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 Analysis Methods”, CONS’13
Recommendation Experiment.Food/Menu Recommender
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
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
User Implicit Feedbacksemi-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 behaviorexternal referrer URL — where the user is coming from
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 importance”, SIGIR’08 “Image Ranking Based on Users Browsing Behavior”, SIGIR’12
User Browsing Graph
User Browsing Graph
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)
User Browsing Graph
“Discovering Social Photo Navigation Patterns”, ICME’12
identifying from where users are entering the website
(external) referrer URL
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?
User Browsing GraphDoes the referrer URL tell us something about the user’s session?
User Browsing GraphDoes the referrer URL tell us something about the user’s session?
User Browsing Graph
search engine
blogs
social network
labeling referrer URLs (top domains)
Does the referrer URL tell us something about the user’s session?
User Browsing Graph
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, …)
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
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 friends
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 same referrer class (domain)
Note: referrer URL comes for free!
The Predictive Power of the Referrer Domain
“Discovering Social Photo Navigation Patterns”, ICME’12
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 ?
User Browsing Graph
User Browsing Graph2 months of logs ~300M page views ~40M user sessions ~10M unique users
Flickr Data
User Browsing Graph2 months of logs ~300M page views ~40M user sessions ~10M unique users
Flickr Data
considering only photo web page
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
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 + stats
centrality based approaches
Photo Ranking Through the Browse Graph
PageRank
BrowseRank
standard approaches explicit + stats
Favorites
Views
View Time
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?
collective attention — implicit visibility of the photo
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?
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?
Photo Ranking Through the Browse Graph
Internal Popularity
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
Photo Ranking Through the Browse Graph
Internal Popularity
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
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
Photo Ranking Through the Browse GraphCollective Attention
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
Photo Ranking Through the Browse GraphCollective Attention
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
Photo Ranking Through the Browse GraphCollective Attention
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
Favorites and Views are not very correlated
Photo Ranking Through the Browse GraphExternal Popularity
Photo Ranking Through the Browse GraphExternal Popularity
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
Photo Ranking Through the Browse GraphExternal Popularity
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
Photo Ranking Through the Browse GraphExternal Popularity
x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features
legend
Photo Ranking Through the Browse GraphExternal 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?
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 photos that have more tags
Photo Ranking Through the Browse Graph
Visual Inspection ~OctoberArtistic 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
Photo Ranking Through the Browse Graph
Visual Inspection ~OctoberArtistic 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
Photo Ranking Through the Browse Graph
Visual Inspection ~OctoberArtistic 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
RecapAnalysis of the Browsing Logs
About the Referrer URL :information about the session the user is going to dounderstanding how the webpages are linked from the external world
RecapAnalysis of the Browsing Logs
About the Referrer URL :information about the session the user is going to dounderstanding how the webpages are linked from the external world
RecapAnalysis of the Browsing Logs
About the BrowseGraph :discovering content “voted” by the usersextending the informativeness with the Referrer URL
About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world
RecapAnalysis of the Browsing Logs
About the BrowseGraph : discovering content “voted” by the users extending the informativeness with the Referrer URL
About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world
RecapAnalysis 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?
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)
browsing graph (referrer graph)
Can we predict the content the user is going to consume?
un-structured
implicit information (navigational patterns)
prediction / recommendation
browsing graph (referrer graph)
Browse Graph on NewsPredicting News Articles Consumption
Browse Graph on NewsPredicting 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.org/2012/09/27/section-2-online-and-digital-news-2
Browse Graph on NewsPredicting News Articles Consumption
Different Context : News Website major portion of the overall Web traffic [1,2]
[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 NewsPredicting News Articles Consumption
Different Context : News Website major portion of the overall Web traffic [1,2]
thousands of news articles per day
[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 NewsPredicting News Articles Consumption
Different Context : News Website major portion of the overall Web traffic [1,2]
thousands of news articles per dayvery short sessions [3]
[3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010
[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 NewsPredicting News Articles Consumption
Different Context : News Website major portion of the overall Web traffic [1,2]
thousands of news articles per dayvery short sessions [3]
many visitors are newcomers
[3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010
[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 NewsPredicting News Articles Consumption
Different Context : News Website major portion of the overall Web traffic [1,2]
thousands of news articles per dayvery short sessions [3]
many visitors are newcomerslinks to news articles shared around the Web
[3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010
Browse Graph on NewsPredicting News Articles Consumption
Browse Graph on NewsPredicting News Articles Consumption
Yahoo News BrowseGraph
~500M pageviews
Browse Graph on NewsPredicting News Articles Consumption
Yahoo News BrowseGraph
~500M pageviews
Social Network Search Engine
Browse Graph on NewsPredicting News Articles Consumption
Yahoo News BrowseGraph
~500M pageviews
Social Network Search Engine
“Cold-start News Recommendation with Domain-dependent Browse Graph”, RecSys’14
Browse Graph on NewsPredicting News Articles Consumption
Yahoo News BrowseGraph
~500M pageviews
Social Network Search Engine
Domain-Dependent BrowseGraph
..or just referrerGraph.
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are differentiable by the referrer domains
Browse Graph on NewsPredicting 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
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of hops during browsing sessions
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of hops during browsing sessions
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of hops during browsing sessions
very different size
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
sessions are very short
average number of hops during browsing sessions
very different size well connected
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importance
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importancehom
epage
yahoo
bin
g
face
book
twitt
er
homepage
yahoo
bing
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
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importancehom
epage
yahoo
bin
g
face
book
twitt
er
homepage
yahoo
bing
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
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Nodes Overlap and Importancehom
epage
yahoo
bin
g
face
book
twitt
er
homepage
yahoo
bing
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
hom
epage
yahoo
bin
g
face
book
twitt
er
homepage
yahoo
bing
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Kendall Between News PageRanks
⌧
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Most Common Categories
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are differentiable by the referrer domains
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
hypothesis : news articles consumed are differentiable by the referrer domains
different graph structuredifferent interest of the users:
individual articles (node)news articles topics
importance (PageRank ranking)
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd]
random
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80popular
• popular [pop]
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80
• popular [pop]
252015
10
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80
• popular [pop]
252015
10
edge
• edge [edge]
first news page
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80
• popular [pop]
252015
10
• edge [edge]
first news page
CB
• content-based [cb](TF-IDF + similarity)
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80
• popular [pop]
252015
10
• edge [edge]
first news page
• content-based [cb](TF-IDF + similarity)
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80
• popular [pop]
252015
10
• edge [edge]
first news page
• content-based [cb](TF-IDF + similarity)
• Full Graph [full]
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
graph selection
• ReferrerGraph [ref]
node selection• random [rnd] 40
30
60
80
• popular [pop]
252015
10
• edge [edge]
first news page
• content-based [cb](TF-IDF + similarity)
• Full Graph [full]• Mix Approach [mix]
+
Precision @1
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)
Precision @1
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)
MRR @3
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)
MRR @3
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
averaged over 1,438 hourly graphs (~350K per hour)
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
About the ReferrerGraph :
Browse Graph on NewsPredicting News Articles ConsumptionYahoo News
BrowseGraph
About the ReferrerGraph :
prediction information of the referrer URL + collective behaviors of the users
Browse Graph on NewsPredicting 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
Browse Graph on NewsPredicting 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
RecapUser Interaction
users browsing traces
RecapUser Interaction
users browsing traces BrowseGraph
RecapUser Interaction
users browsing traces BrowseGraph
RecapUser Interaction
users browsing traces BrowseGraph
prediction and
personalization
RecapUser Interaction
User interactionsFuture Work
User interactionsFuture Work
Extending Implicit Signals location data (IP Address, Mobile GPS)device type (tablet vs. mobile vs. desktop)custom webpage data (Social Media, …)
User interactionsFuture Work
Extending Implicit Signals location data (IP Address, Mobile GPS)device type (tablet vs. mobile vs. desktop)custom webpage data (Social Media, …)
Integrating User Profilelong term user informationuser’s profile changes over time (with respect to the referrer?)
User interactionsFuture Work
Extending Implicit Signals location data (IP Address, Mobile GPS)device type (tablet vs. mobile vs. desktop)custom webpage data (Social Media, …)
Integrating User Profilelong term user informationuser’s profile changes over time (with respect to the referrer?)
Experiment Different Graphsgraph of actions instead of pageviews? (share actions, explicit activity, ads, …)
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-structured
structured
Capturing Insights from Users ActionsBeyond Explicit Information
michele trevisiol
Questions?
micheletrevisiol.com
un-semst
Capturing Insights from Users ActionsBeyond Explicit Information
michele trevisiol2015
Questions?
micheletrevisiol.com
un-semst