The Needs of the Many: A Case-Based Group Recommender System

2006 ◽  
pp. 196-210 ◽  
Author(s):  
Kevin McCarthy ◽  
Lorraine McGinty ◽  
Barry Smyth ◽  
Maria Salamó
Keyword(s):  
Recommender System ◽  
The Many ◽  
Group Recommender ◽  
Case Based

scholarly journals MOOC-Rec: A Case Based Recommender System for MOOCs

2015 ◽  
Vol 195 ◽  
pp. 1813-1822 ◽  
Author(s):  
Fatiha Bousbahi ◽  
Henda Chorfi
Keyword(s):  
Recommender System ◽  
Case Based

Tracing the Many Translations of a Web-Based IT Artefact

2010 ◽  
pp. 1668-1688
Author(s):  
R. Naidoo ◽  
A. Leonard
Keyword(s):  
Actor Network Theory ◽  
Research Strategy ◽  
Web Based ◽  
Healthcare Insurance ◽  
Self Service ◽  
The Many ◽  
Self Service Technology ◽  
Human Actors ◽  
Complementary Channel ◽  
Case Based

This chapter adopts an interpretive, case based research strategy to discuss the centrality of meaning in implementing an Internet-based self-service technology. Actor-Network theory (ANT) is used to describe the complex evolution of a Web-based service at a healthcare insurance firm. Using processes of inscribing, translating and framing, this chapter explores the emergence of the technology from 1999 – 2005 using three technological frames, ‘channel of choice’, ‘dazzle the customer’, and ‘complementary channel’ as episodes of translation. ANT demonstrates that the Internet-based self-service technology at this particular healthcare context emerged out of many unplanned negotiations and mediations with both human and non human actors. Finally, this chapter argues that ANT’s socio-technical lens provides a richer understanding of the meaning of Internet-based self-service technology within a multi-channel context.

Task Allocation in Case-Based Recommender Systems

2011 ◽  
pp. 268-279 ◽  
Author(s):  
Fabiana Lorenzi ◽  
Daniela Scherer dos Santos ◽  
Denise de Oliveira ◽  
Ana L.C. Bazzan
Keyword(s):  
Swarm Intelligence ◽  
Recommender Systems ◽  
Recommender System ◽  
Task Allocation ◽  
User Preferences ◽  
Cooperative Agents ◽  
Over Time ◽  
Case Based

Case-based recommender systems can learn about user preferences over time and automatically suggest products that fit these preferences. In this chapter, we present such a system, called CASIS. In CASIS, we combined the use of swarm intelligence in the task allocation among cooperative agents applied to a case-based recommender system to help the user to plan a trip.

A novel group recommender system based on members’ influence and leader impact

2020 ◽  
Vol 205 ◽  
pp. 106296
Author(s):  
Reza Barzegar Nozari ◽  
Hamidreza Koohi
Keyword(s):  
Recommender System ◽  
Group Recommender

CBR for CBR: A Case-Based Template Recommender System for Building Case-Based Systems

2008 ◽  
pp. 459-473 ◽  
Author(s):  
Juan A. Recio-García ◽  
Derek Bridge ◽  
Belén Díaz-Agudo ◽  
Pedro A. González-Calero
Keyword(s):  
Recommender System ◽  
Case Based
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