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Exploiting Tag-Based Personalization for Recommendation on Social Web Frederico Dur

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... recommender systems and search engine Research Overview Intuition, ... Semantic Similarity Model ... Web search personalization with ontological user profiles. – PowerPoint PPT presentation

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Title: Exploiting Tag-Based Personalization for Recommendation on Social Web Frederico Dur


1
Exploiting Tag-Based Personalization for
Recommendation on Social Web Frederico
Durão Aalborg Denmark 13.02.2012
Advised by Prof. Peter Dolog
2
Outline
  • Introduction
  • Motivation, problem statement, research questions
  • Literature Review
  • User modeling, recommender systems and search
    engine
  • Research Overview
  • Intuition, model and achievements
  • Conclusion and Future Works
  • Contributions and looking ahead

3
Motivation
  • The World Wide Web was born in the early nineties
    when Tim Berners-Lee had the idea of sharing
    information between scientists from remote
    laboratories Tim Berners-Lee and Mark Fischett,
    2000.
  • The Web 2.0 has instituted decentralized content
    creation, thus leading the Web into a more open,
    connected, and democratic environment Tim
    OReilly, 2005.
  • Social Web an environment for online
    communication and collaboration in which user
    participation is the primary driver of value
    Breslin, 2009.
  • An underlying feature of social web applications,
    Collaborative Tagging allows users to assign
    keywords, known as TAGS, to resources on the Web
    such as photos, videos, and websites.

4
Social Tagging Applications
Tags
5
Tagging ltuser, tag, resourcegt
  • User
  • You, me, a system, someone who assigns a tag to a
    resource
  • Tag
  • a word, or a set of words that describe a
    resource
  • Resource
  • text, link, bookmark, image, video, you decide

user
Picture of Maradona
Tags
Tags
Picture of Pele
6
Personalized Tag-based Recommendation
Tag Amount
F1 100
Ferrari 70
BMW 50
Racing 20
Woman 2
Tag Amount
Travel 80
Beach 40
Paradise 50
Holidays 20
Work 1
7
Problems
  • The exposure of users to both tags and resources
    creates the immense availability of social data
    on the Web.
  • Coping with such amount of information becomes
    critical since users on the Web are heterogeneous
    and have distinct interests.

People are different in taste and preferences
How to make proper recommendations ?
8
Research Questions
  • RQ1 How can we learn and rank user preferences
    from tagging information for personalization?
  • RQ2 How can we personalize information using
    tag-based user profiles for social web
    applications?
  • RQ3 How can social aspects impact the
    performance of tag-based personalization models
    for social web applications?

9
Literature Review
  • User Modeling
  • An essential activity for maintaining information
    about the users knowledge, beliefs, goals,
    abilities, attitudes, and preferences. Peter
    Brusilovsky, 2001
  • Recommender Systems
  • Software tools that support users to make
    decisions among various alternatives by
    suggesting items that could be of interest of
    them. Tariq Mahmood and Francesco Ricci, 2009
  • Search Engines
  • Computer programs that support users to find
    specific piece of information within vast
    collection of documents. Ricardo Baeza-Yates
    and Berthier Ribeiro-Neto, 1999

10
Related Work
  • User Modeling

Stereotype-Based - User Models for E-Commerce
Tagging-Based
Highlights
11
Related Work (2)
  • Recommender Systems

Collaborative Filtering - Content-Based
- PageRank/FolkRank
Highlights
(ratings) - (item
features/tags) - (links/taggings)
12
Related Work (3)
  • Search Engine

User Clicks - Query Log - Query Expansion -
Social Search
Highlights
13
Research Overview
Paper N. Title Research Question
1 A Personalized Tag-Based Recommendation in Social Web Systems 1,2
2 Extending a Hybrid Tag-based Recommender System with Personalization 2
3 A Multi-Factor Tag-Based Personalized Search 1,2
4 Social and Behavioral Aspects of a Tag-based Recommender System 3
5 Recommending Open Linked Data in Creativity Sessions using Web Portals with Collaborative Real Time Environment 2,3
6 Improving Tag-Based Recommendation with the Collaborative Value of Wiki Pages for Knowledge Sharing 1,2,3
14
Paper 1 A Personalized Tag-Based Recommendations
in Social Web Systems
  • Assumption
  • Resources that share same tags
  • are likely to be related to each other.
  • Personalization Tag-Based Model
  • to recommend unknown documents dmax,u
    for all user , which maximize the
    personalized function persRec as
  • Multifactor Model

(Tags football, sports)
(Tags reading, books)
(Tags football, books)
15
Paper 1 A Personalized Tag-Based Recommendations
in Social Web Systems
  • Evaluation Goal (Qualitative Assessment of the
    Tag-based Recommender)
  • Assess the efficiency of the proposed approach by
    measuring the degree of satisfaction of users
    about the received recommendations.
  • Methodology
  • 38 participants from 12 countries
  • Participant -gt Del.icio.us account -gt 10
    bookmarks at least
  • 5542 tags and 1143 bookmarks.
  • Experiment Results

Recommendations Accepted Rejected
5/5 59 41
gt 3/5 58 42
16
Paper 2 Extending a Hybrid Tag-based Recommender
System with Personalization
  • Assumption
  • Syntax variations reduces considerably the
    chances of finding tag similarity.
  • Example tags Berlin and Germany are related
    and should be considered in the similarity
    calculus.
  • Semantic Similarity Model the semantic factor
  • SemanticSimilarity(tag1,tag2)
    WordNet(tag1,tag2) x Ontology(tag1, tag2)
  • Example
  • SemanticSimilarity(Berlin,Germany)

17
Paper 2 Extending a Hybrid Tag-based Recommender
System with Personalization
  • Semantic Group Achievements
  • Evaluation / Comparison with previous study
  • Example of tag sets similarity calculus

WordNet Ontologies Both
45 24 31
Precision Rates Precision Rates
Recommendations Accepted Items Rejected Items
Pure Syntax-Based 70 57
Semantic Factor 77 40
18
Paper 3 A Multi-Factor Tag-Based Personalized
Search
(2)
(1)
  • Assumption
  • Multiples indicators of user preference
  • can better determine what is more or less
  • relevant than a single one.
  • Multi-factor Personalized Search Model
  • Tag Frequency
  • Weighing Factor
    Example
  • One factor can be more important that the other.

(3)
Factor Weigh
Users personal tags (1) 0.6
Tags assigned to favorite pages (2) 0.3
Tags assigned to frequent visited pages (3) 0.1
19
Paper 3 A Multi-Factor Tag-Based Personalized
Search
  • Evaluation
  • MovieLens dataset 1,147,048 ratings and 95,580
    tags applied to 10,681 movies by 13,190 users.
  • Comparison of our approach against 6 similarity
    methods
  • Cosine Similarity, Matching Coefficient, Dice,
    Jaccard and Euclidean Distance
  • Results

61.6 of precision improvement over traditional
text-based information retrieval (non-pers)
and 6,13 of precision gain over cosine
similarity, the best method compared
20
Paper 4 Social and Behavioral Aspects of a
Tag-based Recommender System
  • Exploratory Research
  • Which social issues interfered in the performance
    recommendations ?
  • Focus on users tagging behavior.
  • Question 1 Why correctly generated
  • recommendations were rejected?
  • Low novelty. Items already known.
  • Items not really interesting.
  • Recommendation written in a language x but
    tagged in English.
  • Therefore, recommendations are correctly
    generated.
  • Ex Some Danish rejected recommendations written
    in Chinese because they simply could not read
    them.

21
Paper 4 Social and Behavioral Aspects of a
Tag-based Recommender System
  • Question 2 How is your purpose while tagging?
  • Question 3 Do you tag only for self
    understating?

Social means use of popular terms
Social tags facilitate Tag-based recommendations
22
Paper 5 Recommending Open Linked Data in
Creativity Sessions Using Web Portals with
Collaborative Real Time Environment
  • Assumption
  • Semantic-based recommendations
  • can support participants in a brainstorming
    sessions.
  • Method
  • Searching semantic relations on the from Linked
    Open Data on the Web.
  • Adapting to a creative technique 5W1H - When,
    Where, Who, What, Why and How

Suggestions for Desert
Camel
Sahara
Dunes
Interrogative Property Mapping
Interrogatives
What
When
Who
Where
How
Ontology Property
type
hasYear
foaf
stateOf
toDo
23
Paper 5 Recommending Open Linked Data in
Creativity Sessions Using Web Portals with
Collaborative Real Time Environment
  • Evaluation
  • Comparison of groups (A, B, C) without semantic
    recommendations against groups with the support
    of recommendations (D, E, F).
  • Results
  • On average 55,9 of the recommendations
    were rated with highest ratings (4-5) whereas
  • 44,1 of the
    recommendations were rated with lowest ratings
    (1-2-3).
  • User comments

24
Paper 6 Improving Tag-Based Recommendation with
the Collaborative Value of Wiki Pages for
Knowledge Sharing
  • Assumption
  • The collaborative value of wiki pages (such as
    Wikipedia) reflects its knowledge sharing
    capacity.
  • We compute this social commitment and used it as
    ranking factor.
  • Intended for problem solving.
  • Method
  • WPCV - Wiki Page Collaborative Value.

Which page should be recommended?
Nelson Mandela
Wiki Page1 Wiki Page 2
Fred
Obama
Silvio Berlusconi
- User Knowledge
- Everything you write, read, tag, comment
increases UK.
- User Interactivity
- You knowledge increases the more interact with
your friends.
25
Paper 6 Improving Tag-Based Recommendation with
the Collaborative Value of Wiki Pages for
Knowledge Sharing
  • Evaluation Methodology
  • 63 participants using a semantic wiki solve a
    given task
  • To ?ll out incomplete wiki pages by collecting
    information placed in other pages of the system.
  • They were required to navigate through the pages
    using our recommendations to ?nd the needed
    information.
  • Two set of recommendations were provided one
    powered by WPCV and the other purely tag-based
    recommendation.
  • Results
  • Qualitative Assessment
  • Subjective Assessment

Improvements of precision, recall and f-measure
at rates of 11, 7 and 12 against PTB.
user participation over time (30 days)
26
Conclusion
  • RESEARCH QUESTION 1
  • Tag-based models to capture and rank user
    preferences by observing user activity including
    tagging, searching, rating, commenting and social
    networking.
  • RESEARCH QUESTION 2
  • Multi-factor personalization model to generate
    user oriented information from tag-based user
    profiles.
  • RESEARCH QUESTION 3
  • Analyses of social and behavioral aspects that
    harm the effectiveness of recommendations.

27
Future Works
  • Flexible user models that allow individuals to
    interact with personalization systems.
  • Use of a time decay factor for adjusting users
    preference over time.
  • Recommendations should invest on diversity to
    avoid redundancy.
  • Attenuate the new user problem by moving towards
    a hybrid approaches.
  • Personalization is never dissociated of criticism
    since it invades the users privacy.

28
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31
Thanks
  • To my family,
  • To my supervisor,
  • To the PhD committee members,
  • To my MI friends,
  • To my IS friends,
  • To my KiWi friends,
  • To my all Casiopeia personnel,
  • To my always helpful secretaries,
  • To my work colleagues,
  • To my close friends in Aalborg,
  • To my M-Eco friends,
  • To your patience with me ?

32
Publications
  • Nothing more than my obligation ?
  • http//vbn.aau.dk/en/persons/frederico-durao(f26ea
    ca1-ec6a-4315-85d4-9c38fa167956)/publications.html

33
Tag-Based Personalization
  • Tagged resources and user tag-based profile are
    collected.
  • Resource tags are compared against user tag-based
    profile.
  • Tag-based personalized are generated.
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