contextual recommendation of social updates, a tag-based framework

Contextual Recommendation of Social Updatesa tag-based framework

Adrien JOLYPhD Candidate, supervisor: Prof. Pierre MARETAlcatel-Lucent Bell Labs France + INSA-Lyon, LIRIS, [email protected] / [email protected]

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


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]


Motivation Approach Framework Evaluation Conclusion Web « 2.0 » social / communication tools

Social Networking Platforms increase Social Awareness




…through Social Updates




But it can steal a lot of attention productivity loss


Motivation Approach Framework Evaluation Conclusion Our proposal

Filter

“Aware” user

Activities/ Status

Updates/ Contacts

Needed Social updates

and productive



1. Motivation

2. Approach

3. Framework

4. Evaluation

5. Conclusion


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


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


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 ! »


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


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


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



1. Motivation

2. Approach

3. Framework

4. Evaluation

5. Conclusion


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


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


Motivation Approach Framework Evaluation Conclusion How to synthesize the contextual tag cloud from web browsing ?

The user opens a web page…


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)


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:


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



2+3. Extracting weighted terms from:

2. Search Query

ambient,

awareness

3. Resource Location

video,

all,

alcatel-Lucent



4. Extracting weighted terms from: Social Annotations

wposter = 11,wwork = 11,wgtd = 10,wdone = 10,

winspiration = 7,…



5. Extracting weighted terms from: Semantic Analysis of

content

MIT,Tim Berners-Lee,

…



1. Motivation

2. Approach

3. Framework

4. Evaluation

5. Conclusion


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


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


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…


Motivation Approach Framework Evaluation Conclusion Survey #1 results 1/2

rarity of good matches (few participants few common tags)


Motivation Approach Framework Evaluation Conclusion Survey #1 results 2/2

Accuracy = 72%(based on MAE between relevance scores and ratings)

Accu

rac

y


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



1. Motivation

2. Approach

3. Framework

4. Evaluation

5. Conclusion


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


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 ?


www.alcatel-lucent.comThank you for your attention!

Your questions are welcome

contextual recommendation of social updates, a tag-based framework

Technology

ais thecontextof

xis thecontextof

extracting

motivation

ais relevant

contextual

contextual

motivation