Predicting and using social tags to improve the accuracy and transparency of recommender systems
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Date
24/11/2011Author
Givon, Sharon
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Abstract
This thesis describes work on using content to improve recommendation systems. Personalised
recommendations help potential buyers filter information and identify products
that they might be interested in. Current recommender systems are based mainly
on collaborative filtering (CF) methods, which suffer from two main problems: (1) the
ramp-up problem, where items that do not have a sufficient amount of meta-data associated
with them cannot be recommended; and (2) lack of transparency due to the fact
that recommendations produced by the system are not clearly explained. In this thesis
we tackle both of these problems.
We outline a framework for generating more accurate recommendations that are
based solely on available textual content or in combination with rating information.
In particular, we show how content in the form of social tags can help improve recommendations
in the book and movie domains. We address the ramp-up problem and
show how in cases where they do not exist, social tags can be automatically predicted
from available textual content, such as the full texts of books. We evaluate our methods
using two sets of data that differ in product type and size. Finally we show how
once products are selected to be recommended, social tags can be used to explain the
recommendations. We conduct a web-based study to evaluate different styles of explanations
and demonstrate how tag-based explanations outperform a common CF-based
explanation and how a textual review-like explanation yields the best results in helping
users predict how much they will like the recommended items.