Electronic Resource

Recommender systems and the social web : leveraging tagging data for recommender systems

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​There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources. The author also proposes algorithms which exploit the user-provided tagging data and produce more accurate recommendations. On the basis of this idea, he shows how tags can be used to explain to the user the automatically generated recommendations in a clear and intuitively understandable form. With his book, Fatih Gedikli gives us an outlook on the next generation of recommendation systems in the Social Web sphere.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

E1519

Penerbit

Wiesbaden : Springer Vieweg Wiesbaden., 2013

Deskripsi Fisik

xi, 112 hlm. : il. warna.

Bahasa

English

ISBN/ISSN

978-3-658-01948-8

Klasifikasi

NONE

Tipe Isi

text

Tipe Media

computer

Tipe Pembawa

online resource

Edisi

-

Subjek

Sistem Rekomendasi : Perangkat Lunak
Algoritma Sistem Rekomendasi

Info Detail Spesifik

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Pernyataan Tanggungjawab

Fatih Gedikli

Versi lain/terkait

Lampiran Berkas

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