Workshop on Context-Aware Recommender Systems (CARS) 2021

被引：1

作者：

Adomavicius, Gediminas ^{[1
]}

Bauman, Konstantin ^{[2
]}

Mobasher, Bamshad ^{[3
]}

Ricci, Francesco ^{[4
]}

Tuzhilin, Alexander ^{[5
]}

Unger, Moshe ^{[5
]}

机构：

[1] Univ Minnesota, Minneapolis, MN 55455 USA

[2] Temple Univ, Philadelphia, PA 19122 USA

[3] De Paul Univ, Chicago, IL 60614 USA

[4] Free Univ Bozen Bolzano, Bolzano, Italy

[5] NYU, New York, NY 10003 USA

来源：

15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021) | 2021年

关键词：

Context-Aware Recommendation; Context; Contextual Modeling; Sequence-Aware Recommendation;

D O I：

10.1145/3460231.3470939

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2021 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

引用

页码：813 / 814

页数：2

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