Abstract
M.Sc. (Information Technology)
Recommender systems assist a system user to identify relevant content within a specific context. This
is typically performed through an analysis of a system user’s rating habits and personal preferences
and leveraging these to return one or a number of relevant recommendations. There are numerable
contexts in which recommender systems can be applied, such as movies, tourism, books, and music.
The need for recommender systems has become increasingly relevant, particularly on the Internet.
This is mainly due to the exponential amount of content that is published online on a daily basis. It has
thus become more time consuming and difficult to find pertinent information online, leading to
information overload. The relevance of a recommender system, therefore, is to assist a system user
to overcome the information overload problem by identifying pertinent information on their behalf.
There has been much research done within the recommender system field and how such systems
can best recommend items to an individual user. However, a growing and more recent research area
is how recommender systems can be extended to recommend items to groups, known as group
recommendation. The relevance of group recommendation is that many contexts of recommendation
apply to both individuals and groups. For example, people often watch movies or visit tourist
attractions as part of a group.
Group recommendation is an inherently more complex form of recommendation than individual
recommendation for a number of reasons. The first reason is that the rating habits and personal
preferences of each system user within the group need to be considered. Additionally, these rating
habits and personal preferences can be quite heterogeneous in nature. Therefore, group
recommendation becomes complex because a satisfactory recommendation needs to be one which
meets the preferences of each group member and not just a single group member.
The second reason why group recommendation is considered to be more complex than individual
recommendation is because a group not only includes multiple personal preferences, but also multiple
personality types. This means that a group is more complex from a social perspective. Therefore, a
satisfactory group recommendation needs to be one which considers the varying personality types
and behaviours of the group.
The purpose of this research is to present PerTrust, a generic framework for group recommendation
with the purpose of providing a possible solution to the aforementioned issues noted above. The
primary focus of PerTrust is how to leverage both personality and trust in overcoming these issues.