Reading PAGE

Peer Evaluation activity

Trusted by 1
Downloads 2
Views 11

Total impact ?

    Send a

    Luis has...

    Trusted 0
    Reviewed 0
    Emailed 0
    Shared/re-used 0
    Discussed 0
    Invited 0
    Collected 0

     

    This was brought to you by:

    block this user Luis De Campos Trusted member

    Professor

    University of Granada, Department of Computer Science and Artificial Intelligence, Granada, Spain

    Managing uncertainty in group recommending processes

    Export to Mendeley

    Abstract While the problem of building recommender systems has attracted considerable attention in recent years, most recommender systems are designed for recommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group recommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes.

    Oh la laClose

    Your session has expired but don’t worry, your message
    has been saved.Please log in and we’ll bring you back
    to this page. You’ll just need to click “Send”.

    Your evaluation is of great value to our authors and readers. Many thanks for your time.

    Review Close

    Short review
    Select a comment
    Select a grade
    You and the author
    Anonymity My review is anonymous( Log in  or  Register )
    publish
    Close

    When you're done, click "publish"

    Only blue fields are mandatory.

    Relation to the author*
    Overall Comment*
    Anonymity* My review is anonymous( Log in  or  Register )
     

    Focus & Objectives*

    Have the objectives and the central topic been clearly introduced?

    Novelty & Originality*

    Do you consider this work to be an interesting contribution to knowledge?

    Arrangement, Transition and Logic

    Are the different sections of this work well arranged and distributed?

    Methodology & Results

    Is the author's methodology relevant to both the objectives and the results?

    Data Settings & Figures

    Were tables and figures appropriate and well conceived?

    References and bibliography

    Is this work well documented and has the bibliography been properly established?

    Writing

    Is this work well written, checked and edited?

    Write Your Review (you can paste text as well)
    Please be civil and constructive. Thank you.


    Grade (optional, N/A by default)

    N/A 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
    Close

    Your mailing list is currently empty.
    It will build up as you send messages
    and links to your peers.

     No one besides you has access to this list.
    Close
    Enter the e-mail addresses of your recipients in the box below.  Note: Peer Evaluation will NOT store these email addresses   log in
    Your recipients

    Your message:

    Your email : Your email address will not be stored or shared with others.

    Your message has been sent.

    Description

    Title : Managing uncertainty in group recommending processes
    Author(s) : Luis M. Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales
    Abstract : Abstract While the problem of building recommender systems has attracted considerable attention in recent years, most recommender systems are designed for recommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group recommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes.
    Keywords : a, b, de campos, f, fernández luna, group recommending, huete, j, l, m, management uncertainty, probabilistic graphical models, rueda morales

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2008

    Affiliations University of Granada, Department of Computer Science and Artificial Intelligence, Granada, Spain
    Journal : User Modeling and User-Adapted Interaction
    Volume : 19
    Issue : 3
    Publisher : Springer Netherlands
    Pages : 207 - 242
    Url : http://www.springerlink.com/index/10.1007/s11257-008-9061-1
    Doi : 10.1007/s11257-008-9061-1

    Leave a comment

    This contribution has not been reviewed yet. review?

    You may receive the Trusted member label after :

    • Reviewing 10 uploads, whatever the media type.
    • Being trusted by 10 peers.
    • If you are blocked by 10 peers the "Trust label" will be suspended from your page. We encourage you to contact the administrator to contest the suspension.

    Does this seem fair to you? Please make your suggestions.

    Please select an affiliation to sign your evaluation:

    Cancel Evaluation Save

    Please select an affiliation:

    Cancel   Save

    Luis's Peer Evaluation activity

    Luis has...

    Trusted 0
    Reviewed 0
    Emailed 0
    Shared/re-used 0
    Discussed 0
    Invited 0
    Collected 0
    Invite this peer to...
    Title
    Start date (dd/mm/aaaa)
    Location
    URL
    Message
    send
    Close

    Full Text request

    Your request will be sent.

    Please enter your email address to be notified
    when this article becomes available

    Your email


     
    Your email address will not be shared or spammed.