MASFA: Mass-collaborative faceted search for online communities
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Faceted search combines faceted navigation with direct keyword search, providing exploratory search capacities allowing progressive query refinement. It has become the de facto standard for e-commerce and product-related websites such as amazon.com and ebay.com. However, faceted search has not been effectively incorporated into non-commercial online community portals such as craigslist.org and medhelp.org. This is mainly because unlike keyword search, faceted search systems require metadata that constantly evolve, making them very costly to build and maintain. In this thesis, we propose a framework, MASFA, which takes a human-machine approach to build and maintain effective faceted search systems free of cost. In MASFA human users, i.e. community members, contribute to the system in a mass-collaborative manner; and machines assist humans based on a set of non-domain-specific techniques. The MASFA approach is completely portable and can be deployed to any application domain supporting a direct search interface. To demonstrate its utility we implemented, deployed, and experimented with MASFA on a subset of Craigslist categories and made it open to public access.