Authors:
Anna Stavrianou
1
and
Magdalini Eirinaki
2
Affiliations:
1
Université Lumière Lyon 2 and Université de Lyon, France
;
2
San Jose State University, United States
Keyword(s):
Forum, Recommender systems, Social networks, Web personalization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Personalized Web Sites and Services
;
Society, e-Business and e-Government
;
Soft Computing
;
Software Agents and Internet Computing
;
Symbolic Systems
;
User Modeling
;
Web 2.0 and Social Networking Controls
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Mining
;
Web Personalization
Abstract:
Web2.0 has resulted in an increasing popularity of personalized recommender systems, especially in the context of social networking applications. Although there exist design approaches available for such systems, most of them make very explicit assumptions on the application domain as well as on the availability and data types to be used as input. In this position paper, we discuss the requirements and challenges of Forum Recommender Systems. Such systems aim at generating automatically posting recommendations for the different user profiles that deal with a forum. Despite the fact that these systems share characteristics with other social media, they have hardly been explored due to the particularities they present in terms of structure, context and user differences. Here, we discuss the particularities of Forum Recommender Systems and we propose a framework that enables the gathering of profile data and the generation of posting recommendations. The proposed framework can also be a
djusted to other social networks.
(More)