Modeling trustworthiness of peer advice in a framework for presenting Web objects that supports peer commenta.

In this paper, we present an approach aimed at enabling users to enrich their experience with web-based objects (texts or videos). In particular, we consider a social network of users offering commentary on the web objects they have experienced together with opinions on the value of this commentary being registered by peers. Within this framework, we integrate a reasoner that personalizes the presentation of these annotations to each new user, selectively limiting what is displayed to promote the commentary that will lead to the most effective knowledge gains, based on a modeling of the trustworthiness of the annotator and the similarity of peers who have found this commentary to be useful. We demonstrate the effectiveness of our approach for selective presentation of these web document annotations by constructing a simulation of knowledge gains achieved by users. Our method is shown to approach the ideal knowledge gains achieved by an optimal algorithm, far outpacing a system where a random selection of commentary is offered (as might match what users would experience if employing self-directed limiting of browsing behaviour). As a result, we offer an effective method for enhancing the experiences of users in contexts with potentially massive amounts of peer commentary.