contextual recommendation of social updates, a tag-based framework
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Contextual Recommendation of Social Updatesa tag-based framework
Adrien JOLYPhD Candidate, supervisor: Prof. Pierre MARETAlcatel-Lucent Bell Labs France + INSA-Lyon, LIRIS, UMR5205adrien.joly@alcatel-lucent.com / adrien.joly@liris.cnrs.fr
All Rights Reserved © Alcatel-Lucent 20102 | AMT’2010, Toronto, Canada | 28/08/2010
Agendaof this presentation
1. Motivation — Awareness and information overload
2. Approach — Context-based filtering
3. Framework — Contextual tag clouds
4. Evaluation — Perceived relevance
5. Conclusion & future work
All Rights Reserved © Alcatel-Lucent 20103 | AMT’2010, Toronto, Canada | 28/08/2010
Social Awareness current/recent
people activities, moods, availability, status…
[Dourish, Ericksson, Gutwin…]
Context Awareness location, surrounding
environment… [Dey’2000]
Motivation Approach Framework Evaluation Conclusion Introduction to Awareness
Awareness is the state or ability to perceive, to feel, or to be conscious of events, objects or sensory patterns […] without necessarily implying understanding.
[wikipedia.org]
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Motivation Approach Framework Evaluation Conclusion Web « 2.0 » social / communication tools
Social Networking Platforms increase Social Awareness
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Motivation Approach Framework Evaluation Conclusion Web « 2.0 » social / communication tools
Social Networking Platforms increase Social Awareness
…through Social Updates
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Motivation Approach Framework Evaluation Conclusion Web « 2.0 » social / communication tools
Social Networking Platforms increase Social Awareness
But it can steal a lot of attention productivity loss
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Motivation Approach Framework Evaluation Conclusion Our proposal
Filter
“Aware” user
Activities/ Status
Updates/ Contacts
Needed Social updates
and productive
All Rights Reserved © Alcatel-Lucent 20108 | AMT’2010, Toronto, Canada | 28/08/2010
Agendaof this presentation
1. Motivation
2. Approach
3. Framework
4. Evaluation
5. Conclusion
All Rights Reserved © Alcatel-Lucent 20109 | AMT’2010, Toronto, Canada | 28/08/2010
Motivation Approach Framework Evaluation Conclusion Filtering possibilities
Motivated goal:
Filter social network updates to enable awareness
without information overload
What criteria should we adopt to find the most relevant
updates ?
Popularity ? (most spread updates)
Response rate ? (most commented updates)
Content-based filtering ? (according to preferences) [Budzik’2000, Bauer’2001]
Collaborative filtering ? (according to similar ratings) [Agosto’2005,
Bielenberg’2005]
Similarity of context
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Motivation Approach Framework Evaluation Conclusion Similarity of context, our hypothesis
CA is the context of a user UA sharing a piece of information IA.
CX is the context of a user UX that is a potential recipient of this information.
AA = Travel in Asia
UA = Alice
IA = « Check out my amazing picture ! »
AB = Working Java
UB = Bob
IB = « What database should I use ? »
AC = Browsing map
UC = Christine
IC = « Looking for holiday locations… »
Hypothesis:
IA is relevant to UX
if CA is similar to CX
All Rights Reserved © Alcatel-Lucent 201011 | AMT’2010, Toronto, Canada | 28/08/2010
Motivation Approach Framework Evaluation Conclusion Similarity of context, our hypothesis
CA is the context of a user UA sharing a piece of information IA.
CX is the context of a user UX that is a potential recipient of this information.
AA = Travel in Asia
UA = Alice
AB = Working Java
UB = Bob
IB = « What database should I use ? »
AC = Browsing map
UC = Christine
IC = « Looking for holiday locations… »
Hypothesis:
IA is relevant to UX
if CA is similar to CX
CA = Travel, Asia
CC = Travel
CB = Java Dev.
Similar context: travel
No relevant matchfor this context
IA = « Check out my amazing picture ! »
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Motivation Approach Framework Evaluation Conclusion What is context ?
Context [Dey, 2001] : « any information that can be used to characterize
the situation of an entity »
From physical sensors:
From computer-based actions:
LocationSurrounding
people Other sensors
Communicationhistory
Web browsinghistory
Documenthistory
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Motivation Approach Framework Evaluation Conclusion From sensors to applications
Context sensorsContext Management
Framework
Applications
Interpretation
Acquisitiondb
Usual representation
scheme for
context information:
Ontology-based
/ semantic
Requires ont. modeling
Lack of semantic data
Complex to
manipulate
Scaling issues
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Updates
Motivation Approach Framework Evaluation Conclusion From sensors to applications
Context Management Framework
Context sensors
Social Applications
Interpretation
Acquisitiondb
Paris Notre-
Dame Café Cloudy
Crowded Sitting with:Pierre
Proposed representation
scheme for
context information:
Contextual tag
clouds
Easy to browse
Easy to edit
Simple &
interoperable
Crowds-friendly
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Agendaof this presentation
1. Motivation
2. Approach
3. Framework
4. Evaluation
5. Conclusion
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Motivation Approach Framework Evaluation Conclusion Context Aggregation and Filtering process
Social updates
Aggregator
Sniffers Notifier
Filter
User
Actionsand tags
Contextualclouds
Notifications
Context Interfaces
Abstractionand weighting
Services
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Motivation Approach Framework Evaluation Conclusion Context Aggregation and Filtering process –- in the enterprise
Social updates
Aggregator
Sniffers Notifier
Filter
User
Actionsand tags
Contextualclouds
Notifications
Context Interfaces
Abstractionand weighting
Services
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Motivation Approach Framework Evaluation Conclusion How to synthesize the contextual tag cloud from web browsing ?
The user opens a web page…
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Motivation Approach Framework Evaluation Conclusion How to synthesize the contextual tag cloud from web browsing ?
Low level and staticauthor description
Automatic contentanalysis
Mining semanticconcepts from content
People-entered tags (wisdom of crowds)
1) URL is sent to the Context Aggregator
2) Content is analyzed by enhancers (including web services)
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Motivation Approach Framework Evaluation Conclusion Contextual Tag Clouds, vector space model and algebra
« Travel » « Asia » « Flight »« Discount
»0.5 0.3 0.1 0.1
),,( 1 nttT Sample tag cloud R:
(normalized) 1,0: iwW
1i
iw
Aggregation of a set V of normalized Tag Clouds normalized sum:
Relevance of Tag Cloud R with S cosine similarity:
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Motivation Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
1. Extracting weighted terms from: Resource Metadata
Title Keywords Description
= 50
= 10
= 1
Parameters
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Motivation Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
2+3. Extracting weighted terms from:
2. Search Query
ambient,
awareness
3. Resource Location
video,
all,
alcatel-Lucent
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Motivation Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
4. Extracting weighted terms from: Social Annotations
wposter = 11,wwork = 11,wgtd = 10,wdone = 10,
winspiration = 7,…
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Motivation Approach Framework Evaluation Conclusion Contextual Tag Clouds, extraction and enhancement functions
5. Extracting weighted terms from: Semantic Analysis of
content
MIT,Tim Berners-Lee,
…
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Agendaof this presentation
1. Motivation
2. Approach
3. Framework
4. Evaluation
5. Conclusion
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Motivation Approach Framework Evaluation Conclusion Requirements and plan
Hypothesis: Recommended social updates are relevant when users’ contexts are similar
To evaluate: Tag cloud similarity for relevance ranking Relevance of social updates to the context of their posting
Experimentation plan:
(1 week) 1 tag cloudevery 10 minutes
2 personalizedsurveys per user
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Motivation Approach Framework Evaluation Conclusion From browsing activity to social matching
Temporal indexingperiod = 10 mn.
Common tags:JAVA, DEV
Common tags:TRAVEL
Recommend u5’ssocial update to u1
Recommend u3’ssocial update to u7
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Motivation Approach Framework Evaluation Conclusion Survey #1
… and 3 social updates with various relevance scores, for each context
upd1
upd2
1 2 3 4
1 2 3 4
Survey #1: For each user, 5 personal contextual clouds are proposed…
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Motivation Approach Framework Evaluation Conclusion Survey #1 results 1/2
rarity of good matches (few participants few common tags)
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Motivation Approach Framework Evaluation Conclusion Survey #1 results 2/2
Accuracy = 72%(based on MAE between relevance scores and ratings)
Accu
rac
y
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Motivation Approach Framework Evaluation Conclusion Survey #2
Survey #2: For each user’s social update,Evaluation of relevance between social updates and context of posting
rating
Results
•Average relevance rating: 50.3% (over 59 social updates), including: - 71% for social bookmark notifications - 38% for tweets ( ≈ 41% of “me now” statuses on twitter [Naaman’2010])
1 2 3 4
All Rights Reserved © Alcatel-Lucent 201032 | AMT’2010, Toronto, Canada | 28/08/2010
Agendaof this presentation
1. Motivation
2. Approach
3. Framework
4. Evaluation
5. Conclusion
All Rights Reserved © Alcatel-Lucent 201033 | AMT’2010, Toronto, Canada | 28/08/2010
Motivation Approach Framework Evaluation Conclusion Contribution
Goal:
Increase awareness, reduce information overload
Proposition:
Use contextual information to rank relevance of social updates
Approach:
Tag-based context representation, instead of ontology-based
Findings (using web browsing activity as context):
Encouraging results: 72% accuracy
Half social updates are relevant to web browsing context,
depending on nature
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Motivation Approach Framework Evaluation Conclusion Future work
Improve quality of contextual tag clouds
Semantic analysis, clustering, and filtering of tags
Dynamic weights (based on time)
Deeper study of social updates
Relevance factors between specific social update and contextual
properties
Gather context from other sources
Additional types of documents (e.g. emails, PDF/word documents…)
Physical context information
Develop a contextual tag cloud manipulation interface (HSI)
Graphical extension, multidimensional/hierarchical tag cloud ?
How to edit tags and their weights ?
All Rights Reserved © Alcatel-Lucent 201035 | AMT’2010, Toronto, Canada | 28/08/2010
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