Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | DDI 2016 - Proceedings of the Workshop on Data-Driven Innovation on the Web |
| ISBN (elektronisch) | 9781450343602 |
| Publikationsstatus | Veröffentlicht - 22 Mai 2016 |
| Veranstaltung | Workshop on Data-Driven Innovation on the Web, DDI 2016 - ACM Web Science Conference, WebSci 2016 - Hannover, Deutschland Dauer: 22 Mai 2016 → 25 Mai 2016 |
Publikationsreihe
| Name | DDI 2016 - Proceedings of the Workshop on Data-Driven Innovation on the Web |
|---|
Abstract
Recommender systems have become indispensable for several Web sites, such as Amazon, Netix and Google News, helping users navigate through the abundance of available choices. Although the field has advanced impressively in the last years with respect to models, usage of heterogeneous information, such as ratings and text reviews, and recommendations for modern applications beyond purchases, almost all of the approaches rely on the data that exist within the recommender and on user explicit input. In a rapidly connected world, though, information is not isolated and does not necessarily lie in the database of a single recommender. Rather, Web offers tremendous amount of information on almost everything, from items to users and their tendency to certain items, but also information on general trends and demographics. We envision an out-of-the-box recommender system that exploits the existing information in a recommender, namely, items, users and ratings, but also explores new sources of information out of the database, like user online traces and online discussions about data items, and exploits them for better and innovative recommendations. We discuss the challenges that such an out-of-the-box approach effects and how it reshapes the field of recommenders.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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- BibTex
- RIS
DDI 2016 - Proceedings of the Workshop on Data-Driven Innovation on the Web. 2016. 2914580 (DDI 2016 - Proceedings of the Workshop on Data-Driven Innovation on the Web).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Recommendations beyond the ratings matrix
AU - Ntoutsi, Eirini
AU - Stefanidis, Kostas
PY - 2016/5/22
Y1 - 2016/5/22
N2 - Recommender systems have become indispensable for several Web sites, such as Amazon, Netix and Google News, helping users navigate through the abundance of available choices. Although the field has advanced impressively in the last years with respect to models, usage of heterogeneous information, such as ratings and text reviews, and recommendations for modern applications beyond purchases, almost all of the approaches rely on the data that exist within the recommender and on user explicit input. In a rapidly connected world, though, information is not isolated and does not necessarily lie in the database of a single recommender. Rather, Web offers tremendous amount of information on almost everything, from items to users and their tendency to certain items, but also information on general trends and demographics. We envision an out-of-the-box recommender system that exploits the existing information in a recommender, namely, items, users and ratings, but also explores new sources of information out of the database, like user online traces and online discussions about data items, and exploits them for better and innovative recommendations. We discuss the challenges that such an out-of-the-box approach effects and how it reshapes the field of recommenders.
AB - Recommender systems have become indispensable for several Web sites, such as Amazon, Netix and Google News, helping users navigate through the abundance of available choices. Although the field has advanced impressively in the last years with respect to models, usage of heterogeneous information, such as ratings and text reviews, and recommendations for modern applications beyond purchases, almost all of the approaches rely on the data that exist within the recommender and on user explicit input. In a rapidly connected world, though, information is not isolated and does not necessarily lie in the database of a single recommender. Rather, Web offers tremendous amount of information on almost everything, from items to users and their tendency to certain items, but also information on general trends and demographics. We envision an out-of-the-box recommender system that exploits the existing information in a recommender, namely, items, users and ratings, but also explores new sources of information out of the database, like user online traces and online discussions about data items, and exploits them for better and innovative recommendations. We discuss the challenges that such an out-of-the-box approach effects and how it reshapes the field of recommenders.
KW - Big data
KW - Data-driven innovation
KW - Information hunting
KW - Recommendation systems
KW - Social data
UR - http://www.scopus.com/inward/record.url?scp=84976347276&partnerID=8YFLogxK
U2 - 10.1145/2911187.2914580
DO - 10.1145/2911187.2914580
M3 - Conference contribution
AN - SCOPUS:84976347276
T3 - DDI 2016 - Proceedings of the Workshop on Data-Driven Innovation on the Web
BT - DDI 2016 - Proceedings of the Workshop on Data-Driven Innovation on the Web
T2 - Workshop on Data-Driven Innovation on the Web, DDI 2016 - ACM Web Science Conference, WebSci 2016
Y2 - 22 May 2016 through 25 May 2016
ER -