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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.