'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Handheld devices have become sufficiently powerful
that it is easy to create, disseminate, and access digital content
(e.g., photos, videos) using them. The volume of such content is
growing rapidly and, from the perspective of each user, selecting
relevant content is key. To this end, each user may run a trust
model - a software agent that keeps track of who disseminates
content that its user finds relevant. This agent does so by
assigning an initial trust value to each producer for a specific
category (context); then, whenever it receives new content, the
agent rates the content and accordingly updates its trust value for
the producer in the content category. However, a problem with
such an approach is that, as the number of content categories
increases, so does the number of trust values to be initially set.
This paper focuses on how to effectively set initial trust values.
The most sophisticated of the current solutions employ predefined
context ontologies, using which initial trust in a given
context is set based on that already held in similar contexts.
However, universally accepted (and time invariant) ontologies
are rarely found in practice. For this reason, we propose a
mechanism called TRULLO (TRUst bootstrapping by Latently
Lifting cOntext) that assigns initial trust values based only on
local information (on the ratings of its user’s past experiences)
and that, as such, does not rely on third-party recommendations.
We evaluate the effectiveness of TRULLO by simulating its use
in an informal antique market setting. We also evaluate the
computational cost of a J2ME implementation of TRULLO on
a mobile phone