Identifying recommendation opportunities for computer-supported collaborative environments

Jesus L. Lobo; Olga C. Santos; Jesus G. Boticario; Javier Del Ser

doi:10.1111/exsy.12159

Identifying recommendation opportunities for computer-supported collaborative environments

Jesus L. Lobo^*, Olga C. Santos, Jesus G. Boticario, Javier Del Ser

^*Corresponding author for this work

Artificial Intelligence

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Collaborative indicators derived from participants' interactions can be used to support and improve their collaborative behaviour. In this research, we focus on automatically identifying recommendation opportunities in the Collaborative Logical Framework from participants' interactions. Different information sources have been considered: (a) statistical collaborative indicators; (b) social interactions; (c) opinions received by the participants via ratings; and (d) users' affective state and personality. The recommendations have been elicited considering the generality and transferability of the participants' interactions provided by the Collaborative Logical Framework. As a result, three scenarios have been identified that lead us to propose meaningful grouping suggestions and recommendations, which ultimately aimed to ground an informed personalized support to the participants in intensive collaborative frameworks.

Original language	English
Pages (from-to)	463-479
Number of pages	17
Journal	Expert Systems
Volume	33
Issue number	5
DOIs	https://doi.org/10.1111/exsy.12159
Publication status	Published - 1 Oct 2016

Keywords

asynchronous interaction
collaborative systems
computer-supported collaborative work
data mining

Access to Document

10.1111/exsy.12159

Cite this

@article{ac8d55683b14481a988ab07d28bd04c8,

title = "Identifying recommendation opportunities for computer-supported collaborative environments",

abstract = "Collaborative indicators derived from participants' interactions can be used to support and improve their collaborative behaviour. In this research, we focus on automatically identifying recommendation opportunities in the Collaborative Logical Framework from participants' interactions. Different information sources have been considered: (a) statistical collaborative indicators; (b) social interactions; (c) opinions received by the participants via ratings; and (d) users' affective state and personality. The recommendations have been elicited considering the generality and transferability of the participants' interactions provided by the Collaborative Logical Framework. As a result, three scenarios have been identified that lead us to propose meaningful grouping suggestions and recommendations, which ultimately aimed to ground an informed personalized support to the participants in intensive collaborative frameworks.",

keywords = "asynchronous interaction, collaborative systems, computer-supported collaborative work, data mining",

author = "Lobo, {Jesus L.} and Santos, {Olga C.} and Boticario, {Jesus G.} and {Del Ser}, Javier",

note = "Publisher Copyright: {\textcopyright} 2016 Wiley Publishing Ltd",

year = "2016",

month = oct,

day = "1",

doi = "10.1111/exsy.12159",

language = "English",

volume = "33",

pages = "463--479",

journal = "Expert Systems",

issn = "0266-4720",

publisher = "Wiley-Blackwell Publishing Ltd",

number = "5",

}

TY - JOUR

T1 - Identifying recommendation opportunities for computer-supported collaborative environments

AU - Lobo, Jesus L.

AU - Santos, Olga C.

AU - Boticario, Jesus G.

AU - Del Ser, Javier

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Collaborative indicators derived from participants' interactions can be used to support and improve their collaborative behaviour. In this research, we focus on automatically identifying recommendation opportunities in the Collaborative Logical Framework from participants' interactions. Different information sources have been considered: (a) statistical collaborative indicators; (b) social interactions; (c) opinions received by the participants via ratings; and (d) users' affective state and personality. The recommendations have been elicited considering the generality and transferability of the participants' interactions provided by the Collaborative Logical Framework. As a result, three scenarios have been identified that lead us to propose meaningful grouping suggestions and recommendations, which ultimately aimed to ground an informed personalized support to the participants in intensive collaborative frameworks.

AB - Collaborative indicators derived from participants' interactions can be used to support and improve their collaborative behaviour. In this research, we focus on automatically identifying recommendation opportunities in the Collaborative Logical Framework from participants' interactions. Different information sources have been considered: (a) statistical collaborative indicators; (b) social interactions; (c) opinions received by the participants via ratings; and (d) users' affective state and personality. The recommendations have been elicited considering the generality and transferability of the participants' interactions provided by the Collaborative Logical Framework. As a result, three scenarios have been identified that lead us to propose meaningful grouping suggestions and recommendations, which ultimately aimed to ground an informed personalized support to the participants in intensive collaborative frameworks.

KW - asynchronous interaction

KW - collaborative systems

KW - computer-supported collaborative work

KW - data mining

UR - http://www.scopus.com/inward/record.url?scp=84978230419&partnerID=8YFLogxK

U2 - 10.1111/exsy.12159

DO - 10.1111/exsy.12159

M3 - Article

AN - SCOPUS:84978230419

SN - 0266-4720

VL - 33

SP - 463

EP - 479

JO - Expert Systems

JF - Expert Systems

IS - 5

ER -

Identifying recommendation opportunities for computer-supported collaborative environments

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this