genius:generic user modeling library for the social semantic web

Delft University of Technology

GeniUS: Generic User Modeling Library for the Social Semantic Web JIST2011, December 2011, Hangzhou, China

Qi Gao, Fabian Abel, Geert-Jan Houben {q.gao, f.abel, g.j.p.m.houben}@tudelft.nl

Web Information Systems Delft University of Technology

2 GeniUS: Generic User Modeling Library for the Social Semantic Web

Personalized Recommendations

Personalized Search Adaptive Systems

What we do: Science and Engineering for the Personal Web

Social Web

Analysis and User Modeling

user/usage data

Semantic Enrichment, Linkage and Alignment

domains: news social media cultural heritage public data e-learning


Motivation

•  Sparsity problem • do not have enough useful information for a (new) user

•  Possible solution: gathering user data from other sources

User Modeling

Product Recommender

I’m a new user. Recommend me some product

? • But not all data may be relevant for the given application context. • how to filter out user data that does not fit the target application context?

?


Research Challenges of GeniUS

Analysis and User Modeling

Semantic Enrichment

Product recommender

Profile

?

Various applications in different domains

Movie recommender

Hotel recommender

interested in:

Movie Product location

customized user profile construction

Movie location

Product recommender

How can we build a flexible and extensible user modeling functionality that adapts to

the demands of a given application context?


What is GeniUS?

• GeniUS is a topic and user modeling software library that

•  produces semantically meaningful profiles to enhance the interoperability of profiles between applications;

•  provides functionality for aggregating relevant information about a user from the Social Web;

•  generates domain-specific user profiles according to the information needs of different applications;

•  is flexible and extensible to serve different applications.


GeniUS: Generic Topic and User Modeling Library for the Social Semantic Web

Item Fetcher Enrichment Weighting

Function

RDF Repository

Filter

Modeling Configuration

RDF Serialization

Social Web

Semantic Web

user data items

enriched items

semantic data

user profiles

interested in:

location product


GeniUS modules: Item Fetcher and Semantic Enrichment

Item Fetcher

Enrichment

Social Web

Twitter API

raw content a <sioc:Post> ; dcterms:created … ; sioc:has_creator …; sioc:content … .

Awesome, love the new Garageband for iPad #apple

Awesome, love the new Garageband for iPad #apple

SpotLight, Zemanta,

OpenCalais

sioc:has_topic dbpedia:Apple_Inc; sioc:has_topic dbpedia:GarageBand; sioc:has_topic dbpedia:Ipad;

dbpedia:GarageBand dbpedia:Ipad dbpedia:Apple_Inc

Garageband iPad #apple


Weighting Function

RDF Serialization

TF TF-IDF

Time-sensitive

RDF Serialization

weight(dbpedia:GarageBand)

weight(dbpedia:Second_Life)

weight(dbpedia:Jazz)

the weighted interests vocabulary

GeniUS modules: Weighting Function and RDF Serialization


(Jazz, 0.5889)

(Second_Life, 0.3114)

(GarageBand, 0.1638)

GeniUS modules: Configuration and Filter Filter



items enriched

items

Filter

Twitter API SpotLight TF

(Second_Life, 0.4101)

(GarageBand, 0.2158)

SELECT DISTINCT ?t WHERE { ? <rdf:type> <dbpedia-owl:Software> }


GeniUS: Generic Topic and User Modeling Library for the Social Semantic Web

Social Web

Semantic Web

GeniUS User Profile interested in:

location … product

Applications

How do user profiles generated by GeniUS support different types of applications?


Analysis of Domain-specific User Profile Construction

• Dataset •  72 Twitter users (CS researchers) observed over a period of 6 months

(> 40,000 tweets) •  a variety of topics mentioned in the tweets

• Research questions •  1. What are the characteristics of (complete) Twitter-based profiles

generated with GeniUS ?

•  2. Can domain-specific profiles be derived from Twitter activities ?

•  3. What are the characteristics of such domain-specific profiles?



0 10 20 30 40 50 60 70

users

0

10

100

1000

10000

# of

twee

ts/e

ntite

s/en

tity

type

stweetsDBPedia entitiesentity types

average number of entities: 1097.1

a potential to generate domain-specific profiles by categorizing entities according to their types

average number of types: 35.0



0 10 20 30 40 50 60 70

users

1

10

100

1000

10000

num

ber o

f ent

ities

generic: all domainsdomain specific: locationsdomain specific: entertainmentdomain specific: products

0 10 20 30 40 50 60 70

users

1

10

100

1000

num

ber o

f ent

ities

domain specific: productssub-domain specific: music productssub-domain specific: bookssub-domain specific: software products

Are the domain-specific user profiles beneficial for supporting different recommendation applications?

×

generic (all domains)

domain: location

domain: entertainment domain: product

the more specific the domain the smaller the profiles

× product

domain: location

domain: entertainment

domain: product


Evaluation of Domain-specific User Profile Construction • Task: Recommending domain-specific tweets

• Domains:

•  three domains: location, entertainment, product

•  three sub-domains of product: book, software, music

• Recommender algorithm: cosine similarity between profile and candidate item

• Ground truth: relevant (re-)tweets of users

• Candidate items: all the tweets posted during evaluation period

time

P(u)= ?

1 month

Recommendations = ?

user profile


Evaluation results

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the domain-specific user modeling strategies improve the performance of recommendations

three different domains


Evaluation results

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)(1;/.'5*8;),6+*<+"

The user modeling quality varies only slightly between the different domains

three sub-domains of product

The sub-domain-specific user modeling strategy also improve the performance of recommendation.


Wrap up

• GeniUS: Generic topic and User modeling library for the Social Semantic Web •  exploits traces (e.g. tweets) that people leave on the Social Web •  enriches the semantics of these traces •  constructs semantic user profiles profile construction can be customized and is adapted to a given application context

• Analysis: •  Twitter-based user profiles contain a great variety of topics •  GeniUS succeeds in generating profiles for different applications and domains

• Evaluation: •  domain-specific user modeling strategies (powered by the semantic filtering of

GeniUS) allow clearly for the best performance •  the more GeniUS adapts to the given domain (and application context) the better

the performance


Qi Gao [email protected] Twitter: @qigaosh http://wis.ewi.tudelft.nl/tweetum/ http://wis.ewi.tudelft.nl/genius/

Thank You!

genius:generic user modeling library for the social semantic web

Education

application

specific profiles

domain specic

generic topic

hastopic dbpedia

user

profiles

product