  Faceted browsing is a commonly supported feature of user interfaces for
access to information. Existing interfaces generally treat facet values
selected by a user as hard filters and respond to the user by only displaying
information items strictly satisfying the filters and in their original ranking
order. We propose a novel alternative strategy for faceted browsing, called
soft faceted browsing, where the system also includes some possibly relevant
items outside the selected filter in a non-intrusive way and re-ranks the items
to better satisfy the user's information need. Such a soft faceted browsing
strategy can be beneficial when the user does not have a very confident and
strict preference for the selected facet values, and is especially appropriate
for applications such as e-commerce search where the user would like to explore
a larger space before finalizing a purchasing decision. We propose a
probabilistic framework for modeling and solving the soft faceted browsing
problem, and apply the framework to study the case of facet filter selection in
e-commerce search engines. Preliminary experiment results demonstrate the soft
faceted browsing scheme is better than the traditional faceted browsing scheme
in terms of its efficiency in helping users navigate in the information space.
