This Small Business Innovation Research (SBIR) Phase I project aims to solve the computational problem of on-demand job matching and scheduling for the purpose of creating an active online listing in the service domain. The recent explosion of online listings reflects the demand by users for Internet-based search for service-based listings. These current listings tend to offer basic post-and-search capability - a digital counterpart of traditional newspaper advertisements. GigBin is a proactive online listing that addresses peoples' needs for fast, personalized, and reliable matching of service seekers and providers by offering a unique technology that enables: a) fast automatic matching of service providers and seekers, and optimized over a wide range of criteria based on dynamic scheduling algorithms; b) personalized searches using machine learning techniques to learn users' preferences over time and recommend better matches; c) reliability - by analysis of the feedback on providers' performance and job statistics to determine reputation score and improve matching recommendations; d) ubiquitous access - uses text understanding to efficiently utilize cell-phone messaging.
Because intelligent matching of service seekers and providers goes beyond search and scheduling, the outcomes of the project should lead to the creation of new job markets. This new approach will impact the long tail of the job market; satisfy casual needs and provide emergency services; predict demand and make it profitable to address niche markets. Most importantly, the tool will impact social change by increasing the welfare of the weakest workforce members who cannot afford advertising costs and marketing efforts. Additionally, this new technology while allowing people to specify requests in natural language, learns personal preferences, tracks progress and reputation, enables payment, and encourages formation of work-centric social networks of service seekers and service providers.
