The proposed work is to investigate the usefulness and feasibility of previously developed big data analysis on the problem of mining people-related information, differentiating people of the same name, aggregating information from different sources, and inferring people related information such as connections from various data sources. The biggest challenge is entity resolution (sometimes also referred to as entity disambiguation or record linkage), in which the same name may refer to different real world entities. For instances, many or even hundreds of people are named "James Smith". So which data is about the same "James Smith" and can be merged and aggregated together is not an easy question. The proposed solution aims to take on this problem and allow users to easily and quickly get information related to a target person on smartphones or tablets without spending one to two hours to do tedious, error-prone people research.
The revolution of Internet has provided a sea of information publicly available. A major part of such information is related to people and their social networks, which are valuable targeted advertisement, sales, marketing, expanding social network, recruiting, job search, etc. Aggregating people-related information is not an easy task. People-information is valuable in various business functions such as recruiting, sales, business development, etc. According to major search engines about one third of search is people search. The proposed work, if successful, could bring people-related information that is currently scattered in many data sources, together without the issue of name ambiguity. This work may also make such information quickly and conveniently to assist business people in networking, making new business connections more effectively and efficiently.
The project is to investigate the commercial usefulness and feasibility of our previously developed big data analysis on the problem of mining people related information, differentiating people of the same name, aggregating information from different sources, and inferring people related information such as connections from various data sources. Benefiting from the NSF iCorps grant, the project has the following outcomes: 1. Market validation and positioning. We have interviewed more than 100 potential users. The interviews have helped us identifying the need and the shortcomings of existing solutions. It helps us better positioning our solutions and identify the sales/recruiting market segment as our initial focus. 2. Identifying a minimum viable product. Through the customer interviews, we have identified the minimum set of functionalities for a viable commercial product based on our technology. 3. Development of an initial prototype and release to beta users. Based on the customer feedback, we have refined our prototypes and released to a set of beta users via browser extensions to plugin into Google search and Linkedin. The responses and feedback from beta users are encouraging and also informative. 4. Team training in commercialization and technology transfer. The iCorps have also helped trained our team to gradually transition from a pure technology-oriented view to a customer-oriented view.
