Analyzing Cross-System User Modeling on the Social Web

download Analyzing Cross-System User Modeling on the Social Web

of 25

  • date post

    28-Aug-2014
  • Category

    Documents

  • view

    1.753
  • download

    1

Embed Size (px)

description

Slides

Transcript of Analyzing Cross-System User Modeling on the Social Web

  • Analyzing Cross-System User Modeling on the Social Web
    ICWE, Cyprus, June 22, 2011
    Fabian Abel, SamurAraujo, QiGao, Geert-Jan Houben
    Web Information Systems, TU Delft
  • What we do: Science and Engineering for the Personal Web
    domains: news social mediacultural heritage public datae-learning
    Personalized
    Recommendations
    Personalized Search
    Adaptive Systems
    Analysis and
    User Modeling
    Semantic Enrichment, Linkage and Alignment
    user/usage data
    Social Web
  • profile
    ?
    Hi, I have a
    new-user problem!
    profile
    Hi, Im back and
    I have new
    interests.
    Hi, I dont know
    that your
    interests changed!
    Pitfalls of User-adaptive Systems
    Hi, Im your new
    user. Give me
    personalization!
    System A
    System D
    System C
    System B
    How can we tackle these problems?
    profile
    profile
    profile
    time
  • Cross-system user modeling on the Social Web
    User data on the Social Web
  • SocialGraph API
    1. get other accounts
    of user
    Account Mapping
    2. aggregate
    public profile
    data
    Social Web Aggregator
    Blog posts:
    Semantic Enhancement
    Profile Alignment
    Bookmarks:
    3. Map profiles to
    target user model
    4. enrich data with
    semantics
    Other media:
    WordNet
    Social networking profiles:
    FOAF
    vCard
    Interweaving public user data with Mypes
    Aggregated,
    enriched profile
    (e.g., in RDF or vCard)
    Google Profile URI
    http://google.com/profile/XY
    Analysis and user modeling
    5. generate user profiles
  • In this paper: User Modeling across Twitter, Flickr and Delicious
    Twitter and Delicious
    1500 users
    80k + 620k TAS
    Flickr and Delicious
    1467 users
    890k + 680k TAS
    Bob
    travel, google IO
    web
    socialmedia
    identity
    This is #interesting:
    http://bit.ly/3gt42f #web
    http://claimid.com
    Twitter
    Delicious
    Flickr
  • Tag-based user profiles
    Tag-based profile of a user u = set of weighted tags:
    weight indicates to what degree
    the user is interested in t
    tag of interest
    Lightweight weighting scheme:
    count how often the user applied the tag
  • Characteristics of tag-based profiles
  • Characteristics of tag-based profiles
    What are the characteristics of the individual tag-based profiles in Twitter, Flickr and Delicious?
    How do the tag-based profiles of individual users overlap between the different systems?
  • Size of tag-based profiles
    Delicious
    Flickr
    Twitter
  • Overlap of tag-based profiles
    Overlap of tag-based profile is less than 10% for more than 90% of the users
  • where:
    - p(t) = probability that t occurs in Tu
    - Tu = tags in user profile P(u)
    Entropy of Tag-based profiles
    Delicious
    Flickr & Delicious
    Flickr
    Twitter & Delicious
    Twitter
    Aggregated profiles reveal wrt entropy significantly more information than the service specific profiles.
  • Observations
    Profile size varies from system to system (e.g. tag-based Twitter profiles are rather sparse)
    Tag-based profiles of an individual user overlap only little(e.g. overlap is less than 10% for more than 90% of the users)
    Entropy of tag-based profiles:
    Twitter < Flickr < Delicious < aggregated profiles
  • Cross-System User Modeling for Cold-start recommendations
  • Evaluation: Recommending tags / bookmarks
    Hi, Im your new
    user. Give me
    personalization!
    delicious
    profile
    profile
    ?
    users tags and bookmarks
    profile
    Ground truth:
    leave-n-out evaluation
    tags to explore
    Cosine-based
    recommender
    Web sites to
    bookmark
    Cross-system
    user modeling
    actual tags and bookmarks of the user
    How does cross-system user modeling impact the recommendation quality (in cold-start situations)?
  • User Modeling Building Blocks
    1. Which tags should be contained in the profile?
    2. Further enrich/align tags?
    3. How to weight the tags?
    1. Source
    Profile?
    tags weights
    analyze
    0.1
    0.1
    0.5
    0.2
    0.1
    t1
    t2
    t3
    t4
    t5
    2. Semantic Enrichment
    enrich
    3. Weighting Scheme
    ?
    weight
    System A
    System B
  • User Modeling Building Blocks (in this talk)
    Source:
    Personal tags from foreign system
    Popular tags from target system
    Semantic Enrichment:
    Enrich tags with similar tags (based on Jaro-Winkler similarity)
    Cross-system rules: if tag A was used in foreign system then add tag B
    Weighting scheme:
    Personal usage frequency in foreign system
    Global usage frquency in target system
    personal
    profile
    popular
    profile
    ?
    similarity
    cross rules
    personal
    global
    Foreign:
    Target:
    a) simJaro(blog, blogs) is high
    b) Cross-system rule:
    blogforeignnikontarget
    web
    blog
    java
    requires profile to compute recommendations
    blogs
    france
  • Cross-System User Modeling for Cold-start recommendations
    Which user modeling strategies performs best in which context?
    How do the different building blocks of the user modeling strategies (e.g. source of user data) influence the quality of the tag-based profiles?
  • Tag recommendations: Twitter / Delicious
    As you can easily see
    :-)
  • Tag recommendations: Twitter Delicious
    Significant improvements regarding all metrics!
    Improvement regarding P@10, but global Delicious trend performs better regarding MRR & S@1.
    Cross-system strategies lead to significant improvement (impact of semantic enrichment is rather low)
    profile
    profile
    profile
    global
    tag frequencies
    (weights)
    profile
    ?
    profile
    ?
    users
    tags
    user profile
    popular
    personal
    personal
    personal
    global
    personal
    global
    global
    baseline
    Cross-system user modeling
    similarity
  • Tag recommendations: Delicious Twitter
    Semantic enrichment (cross-system rules) allow for significant improvement regarding P@10
    Significant improvements regarding all metrics!
    profile
    profile
    profile
    Tag-based profile information from Delicious seems to be more valuable than hashtga-based Twitter profiles
    users tags
    and tag frequencies (weights)
    profile
    ?
    user profile
    popular
    personal
    personal
    personal
    global
    personal
    global
    global
    baseline
    Cross-system user modeling
    crossrules
  • Tag Recommendations: different settings
    profile
    profile
    target:
    Cross-system user modeling allows for cold-start tag recommendations in Delicious:
    Twitter profiles are more appropriate than Flickr profiles.
    Cross-system user modeling is also beneficial for cold-start tag recommendations in Flickr.
    target:
    profile
    ?
    profile
    ?
    Cross-system user modeling has significant impact on the recommendation performance
    To optimize the performance one adapt to the given application setting
    profile
  • Bookmark Recommendations
    Cross-system user modeling achieves also significant improvements for cold-start bookmark recommendations
    Twitter is again a more appropriate source than Flickr
    baseline
    Cross UM
    Cross UM
  • Conclusions
    Characteristics of distributed tag-based profiles:
    Overlap of tag-based profiles, which an individual user creates at different services, is low
    Aggregated profiles reveal significantly more information (regarding entropy) than service-specific profiles
    Performance of cross-system user modeling for cold-start recommendations:
    Cross-system UM leads to tremendous (and significant) improvements of the tag and bookmark recommendation quality
    To optimize the performance one has to adapt the cross-system strategies to the concrete application setting
    http://persweb.org
  • Thank you!
    Fabian Abel, QiGao, Geert-Jan Houben, Ke Tao
    Datasets: http://wis.ewi.tudelft.nl/icwe2011/um/
    Twitter: @persweb
    http://persweb.org