Recommender Systems:
Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Somerset
Wiley
2014
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Zusammenfassung: | Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (253 pages) |
| ISBN: | 9781119054245 9781119054252 |
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| 520 | |a Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior | ||
| 650 | 4 | |a Recommender systems (Information filtering) | |
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| 700 | 1 | |a Saleh, Imad |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| author | Kembellec, Gérald |
| author_facet | Kembellec, Gérald |
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| author_sort | Kembellec, Gérald |
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| dewey-search | 001.64 |
| dewey-sort | 11.64 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Allgemeines |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV043891945 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:37:50Z |
| institution | BVB |
| isbn | 9781119054245 9781119054252 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029301326 |
| oclc_num | 897810260 |
| open_access_boolean | |
| physical | 1 online resource (253 pages) |
| psigel | ZDB-30-PQE ZDB-38-ESG |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Wiley |
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| spelling | Kembellec, Gérald Verfasser aut Recommender Systems 1st ed Somerset Wiley 2014 © 2014 1 online resource (253 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior Recommender systems (Information filtering) Chartron, Ghislaine Sonstige oth Saleh, Imad Sonstige oth Erscheint auch als Druck-Ausgabe Kembellec, Gérald Recommender Systems |
| spellingShingle | Kembellec, Gérald Recommender Systems Recommender systems (Information filtering) |
| title | Recommender Systems |
| title_auth | Recommender Systems |
| title_exact_search | Recommender Systems |
| title_full | Recommender Systems |
| title_fullStr | Recommender Systems |
| title_full_unstemmed | Recommender Systems |
| title_short | Recommender Systems |
| title_sort | recommender systems |
| topic | Recommender systems (Information filtering) |
| topic_facet | Recommender systems (Information filtering) |
| work_keys_str_mv | AT kembellecgerald recommendersystems AT chartronghislaine recommendersystems AT salehimad recommendersystems |