Dynamically constructing user profiles with similarity-based online incremental clustering

Roman Y. Shtykh, Qun Jin

    Research output: Contribution to journalArticle

    6 Citations (Scopus)

    Abstract

    User profiling is a widely used technique to analyse and store user interests and preferences to apply this knowledge to improve user experiences with information systems. In this research paper, we present an approach for dynamically constructing user profiles, particularly from uniform relevance feedback in information-seeking activities. We propose an inference method for user interests, which we call High-Similarity Sequence Data-Driven (H2S2D) clustering and discuss its peculiarities and show its superiority for the creation of high-quality concepts, which are the elementary constituents of user profiles. To reflect the volatility of user interests and emphasise the steadiness of persistent preferences, we adopt recency, frequency and persistency as the three main criteria for multi-layered dynamic profile construction and update.

    Original languageEnglish
    Pages (from-to)377-397
    Number of pages21
    JournalInternational Journal of Advanced Intelligence Paradigms
    Volume1
    Issue number4
    DOIs
    Publication statusPublished - 2009 Jun

    Fingerprint

    User Profile
    Information systems
    Clustering
    Feedback
    User Profiling
    Relevance Feedback
    User Experience
    Data-driven
    Volatility
    Information Systems
    Update
    Similarity

    Keywords

    • High-similarity sequence data-driven clustering
    • Interest dynamics
    • Multi-layered user profile
    • Relevance feedback
    • User model

    ASJC Scopus subject areas

    • Computer Science(all)
    • Engineering(all)
    • Applied Mathematics

    Cite this

    Dynamically constructing user profiles with similarity-based online incremental clustering. / Shtykh, Roman Y.; Jin, Qun.

    In: International Journal of Advanced Intelligence Paradigms, Vol. 1, No. 4, 06.2009, p. 377-397.

    Research output: Contribution to journalArticle

    @article{b81bf7cb75b54c4c87e848b455556c1f,
    title = "Dynamically constructing user profiles with similarity-based online incremental clustering",
    abstract = "User profiling is a widely used technique to analyse and store user interests and preferences to apply this knowledge to improve user experiences with information systems. In this research paper, we present an approach for dynamically constructing user profiles, particularly from uniform relevance feedback in information-seeking activities. We propose an inference method for user interests, which we call High-Similarity Sequence Data-Driven (H2S2D) clustering and discuss its peculiarities and show its superiority for the creation of high-quality concepts, which are the elementary constituents of user profiles. To reflect the volatility of user interests and emphasise the steadiness of persistent preferences, we adopt recency, frequency and persistency as the three main criteria for multi-layered dynamic profile construction and update.",
    keywords = "High-similarity sequence data-driven clustering, Interest dynamics, Multi-layered user profile, Relevance feedback, User model",
    author = "Shtykh, {Roman Y.} and Qun Jin",
    year = "2009",
    month = "6",
    doi = "10.1504/IJAIP.2009.026760",
    language = "English",
    volume = "1",
    pages = "377--397",
    journal = "International Journal of Advanced Intelligence Paradigms",
    issn = "1755-0386",
    publisher = "Inderscience Enterprises Ltd",
    number = "4",

    }

    TY - JOUR

    T1 - Dynamically constructing user profiles with similarity-based online incremental clustering

    AU - Shtykh, Roman Y.

    AU - Jin, Qun

    PY - 2009/6

    Y1 - 2009/6

    N2 - User profiling is a widely used technique to analyse and store user interests and preferences to apply this knowledge to improve user experiences with information systems. In this research paper, we present an approach for dynamically constructing user profiles, particularly from uniform relevance feedback in information-seeking activities. We propose an inference method for user interests, which we call High-Similarity Sequence Data-Driven (H2S2D) clustering and discuss its peculiarities and show its superiority for the creation of high-quality concepts, which are the elementary constituents of user profiles. To reflect the volatility of user interests and emphasise the steadiness of persistent preferences, we adopt recency, frequency and persistency as the three main criteria for multi-layered dynamic profile construction and update.

    AB - User profiling is a widely used technique to analyse and store user interests and preferences to apply this knowledge to improve user experiences with information systems. In this research paper, we present an approach for dynamically constructing user profiles, particularly from uniform relevance feedback in information-seeking activities. We propose an inference method for user interests, which we call High-Similarity Sequence Data-Driven (H2S2D) clustering and discuss its peculiarities and show its superiority for the creation of high-quality concepts, which are the elementary constituents of user profiles. To reflect the volatility of user interests and emphasise the steadiness of persistent preferences, we adopt recency, frequency and persistency as the three main criteria for multi-layered dynamic profile construction and update.

    KW - High-similarity sequence data-driven clustering

    KW - Interest dynamics

    KW - Multi-layered user profile

    KW - Relevance feedback

    KW - User model

    UR - http://www.scopus.com/inward/record.url?scp=79955740793&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=79955740793&partnerID=8YFLogxK

    U2 - 10.1504/IJAIP.2009.026760

    DO - 10.1504/IJAIP.2009.026760

    M3 - Article

    VL - 1

    SP - 377

    EP - 397

    JO - International Journal of Advanced Intelligence Paradigms

    JF - International Journal of Advanced Intelligence Paradigms

    SN - 1755-0386

    IS - 4

    ER -