rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Improving Content-Aware Recommendation: Relationship Mining, Integrating, and Disentangling
  name:
    name part: Zhang, Yin
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Caverlee, James
    role:
      role term: thesis advisor.
  name:
    name part: Texas A&M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Explosive growth in the amount of information has infiltrated every aspect of our lives. Recommender systems, as an effective tool to filter information, have become prevalent and influential in many domains. This dissertation seeks to improve recommender systems by learning the unique properties and complex relationships of the rich content information associated with both users (who seek recommendations) and items (that are recommended to users). However, to do so, there are key research challenges: (i) the extreme sparsity of the observed item relations; (ii) high het-erogeneity between user-user relations and user-item interactions; (iii) duplication between content and collaborative signals; and (iv) the highly skewed long-tail distribution of user feedback towards items. With these challenges, this dissertation research makes four unique contributions: ⁰́Ø The first research contribution of this dissertation is to mine and integrate the item-side relations. Concretely, we begin by investigating different types of item relations (e.g., complementary and substitute relations), and build a novel neural item-relationship based model to uncover the item relationships. We then integrate these item multi-relations into user sequences through a hierarchical temporal graph to enhance sequential recommendation. ⁰́Ø The second research contribution of this dissertation is to mine and integrate the user-side relations. We are one of the first to investigate the socio-behavioral phenomenon of social resonance to closely connect user relations with user interactions towards items. Then we integrate the user social relations in sequential recommendations to improve fashion recommendations. We also provide a new large visual dataset of dynamic visual posts by key fashion bloggers that contain both high-quality dynamic fashion preference shifts over time. ⁰́Ø The third research contribution of this dissertation is to disentangle the content and collab-orative features to addre…
  note: by Yin Zhang.
  note: "Major Subject: Computer Engineering"
  note: Includes vita.
  note: Doctor of Philosophy Texas A&M University 2022 https://hdl.handle.net/1969.1/197113
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Computer Engineering
  subject:
    topic: Recommender Systems
  subject:
    topic: Machine Learning
  subject:
    topic: Disentangling
  subject:
    topic: Relationship Mining
  subject:
    topic: Fashion Recommendation
  identifier: https://hdl.handle.net/1969.1/197113
  record info:
    record content source: TXA
    record creation date: 240819
    record change date: 20240820145844.5
    record identifier: in00005463432
    language of cataloging:
      language term: eng