A review of the impacts of linked open data on cross-domain recommender systems for individual and groups

Abstract

As users' viewpoints on information searching change from information seeking to information receiving, new search paradigms are continuously emerging. Utilizing a recommender system (RS) is one of the modern ways to get information. The RS has succeeded in various traditional domains, including tourism, health, and books. However, some scenarios are more suitable to recommend to a group of users than an individual, such as listening to music at the same place and group traveling. The limited and incomplete number of user-item ratings triggers the challenges of the group and individual RSs. The data sparsity problem emerges because of this incompleteness. The quality of recommendations offered to individuals and groups suffers when there is data sparsity. Using knowledge gained from a source domain, cross-domain RSs can enhance recommendations in target domain. Cross-domain and linked open data approaches are two ways to increase recommendation systems' performance. The impacts of the two aforementioned approaches on individual and group RSs have been discussed. Furthermore, we highlighted various domains employed in cross-domain RSs for individuals and groups, examined diverse methodologies and algorithms, outlined current issues, and suggested future directions for cross-domain RSs research for groups leveraging linked open data technology.