scholarly journals A Comparative Study of Compound Critique Generation in Conversational Recommender Systems

2006 ◽  
pp. 234-243 ◽  
Author(s):  
Jiyong Zhang ◽  
Pearl Pu
Keyword(s):  
Comparative Study ◽  
Recommender Systems

A Comparative Study of Recommender Systems

2020 ◽  
pp. 1021-1029
Author(s):  
Yash Mehta ◽  
Aditya Singhania ◽  
Ayush Tyagi ◽  
Pranav Shrivastava ◽  
Mahesh Mali
Keyword(s):  
Comparative Study ◽  
Recommender Systems

Comparative study of recommender systems using movie rating data

2020 ◽  
Vol 31 (6) ◽  
pp. 975-991
Author(s):  
Seunghwan Lee ◽  
Youngsang Cho ◽  
Jun Seok Lee ◽  
Donghyeon Yu
Keyword(s):  
Comparative Study ◽  
Recommender Systems ◽  
Rating Data

scholarly journals Context-Aware Music Recommender Systems for Groups: A Comparative Study

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 506
Author(s):  
Adrián Valera ◽  
Álvaro Lozano Murciego ◽  
María N. Moreno-García
Keyword(s):  
Comparative Study ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Context Aware ◽  
The Past ◽  
Single User ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Music Streaming ◽  
Group Recommender

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

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