Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "4914"
  #_source: array:26 [
    "id" => "4914"
    "slug" => "offering-collaborative-like-recommendationis-when-data-is-sparse-the-case-of-attraction-weighted-information-filtering"
    "yearMonth" => "2004-01"
    "year" => "2004"
    "title" => "Offering Collaborative-like Recommendationis When Data is Sparse: The Case of Attraction-weighted Information Filtering"
    "description" => "DE BRUYN, A., GILES, C.L. et PENNOCK, D.M. (2004). Offering Collaborative-like Recommendationis When Data is Sparse: The Case of Attraction-weighted Information Filtering. Dans: <i>Lectures Notes in Computer Science n° 3137. Proceedings of the Third International Conference on Adaptive Hypermedia and Adaptive Web-based Systems</i>. Springer, pp. 393-396."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "DE BRUYN Arnaud"
        "bid" => "B00024825"
        "slug" => "de-bruyn-arnaud"
      ]
      1 => array:1 [
        "name" => "GILES C.L."
      ]
      2 => array:1 [
        "name" => "PENNOCK D.M."
      ]
    ]
    "ouvrage" => "Lectures Notes in Computer Science n° 3137. Proceedings of the Third International Conference on Adaptive Hypermedia and Adaptive Web-based Systems"
    "keywords" => []
    "updatedAt" => "2021-04-19 17:57:25"
    "publicationUrl" => null
    "publicationInfo" => array:3 [
      "pages" => "393-396"
      "volume" => null
      "number" => null
    ]
    "type" => array:2 [
      "fr" => "Actes d'une conférence"
      "en" => "Conference Proceedings"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "Nous proposons un algorithme de recommandations en ligne permettant de transformer des recommandations basées sur la similarité de contenus en recommandations collaboratives requérant moins de données que les systèmes classiques. Une première étude dans un contexte de vente de livres en ligne indique que notre approche est compétitive face à des systèmes collaboratifs plus lourds."
      "en" => "We propose a low-dimensional weigthing scheme to map information filtering recommendations into more relevant, collaborative filtering-like recommendations where items are weigthed by attraction indexes representing existing customers' preferences. A first study conducted with consumers within an online bookseller context indicates that recommendations made by our system favorably compare to data-hungry collaborative filtering systems, while requiring much less data."
    ]
    "authors_fields" => array:2 [
      "fr" => "Marketing"
      "en" => "Marketing"
    ]
    "indexedAt" => "2024-12-22T01:21:46.000Z"
    "docTitle" => "Offering Collaborative-like Recommendationis When Data is Sparse: The Case of Attraction-weighted Information Filtering"
    "docSurtitle" => "Actes d'une conférence"
    "authorNames" => "<a href="/cv/de-bruyn-arnaud">DE BRUYN Arnaud</a>, GILES C.L., PENNOCK D.M."
    "docDescription" => "<span class="document-property-authors">DE BRUYN Arnaud, GILES C.L., PENNOCK D.M.</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">2004</span>"
    "keywordList" => ""
    "docPreview" => "<b>Offering Collaborative-like Recommendationis When Data is Sparse: The Case of Attraction-weighted Information Filtering</b><br><span>2004-01 | Actes d'une conférence </span>"
    "docType" => "research"
    "publicationLink" => "<a href="#" target="_blank">Offering Collaborative-like Recommendationis When Data is Sparse: The Case of Attraction-weighted Information Filtering</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 9.054154
  +"parent": null
}