Trust relationships between users in various online communities are notoriously hard to model for computer scientists.
It can be easily verified that trying to infer trust based on the social network alone
is often inefficient.
Therefore,
the avenue we explore is applying Data Mining algorithms to unearth latent relationships and patterns from background data.
In this paper, we focus on a case where the background data is
user ratings for online product reviews.
We consider as a testing ground a large dataset provided by Epinions.com that
contains a trust network as well as user ratings for reviews on
products from a wide range of categories.
In order to predict trust we define and compute a critical set of
features, which we show to be highly effective in providing the basis
for trust predictions.
Then, we show that state-of-the-art classifiers can do an impressive
job in predicting trust based on our extracted features.
For this, we employ a variety of measures to evaluate the classification
based on these features.
We demonstrate that by carefully collecting and synthesizing
readily available background information, such as ratings for online reviews,
one can accurately predict trust-based social links. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3722 |
| Date | 13 December 2011 |
| Creators | Korovaiko, Nikolay |
| Contributors | Thomo, Alex |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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