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System and Method for Personalized Search Criteria Disclosure Number: IPCOM000249416D
Publication Date: 2017-Feb-27
Document File: 3 page(s) / 147K

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The Prior Art Database


Disclosed is a system that recommends unconventional job opportunities to a person based on not only professionals qualifications, but also previously learned likes/dislikes, personality, additional activities (e.g., hobbies) and determined soft skills. The system collects data from social networks and applies cognitive analytics to identify additional personal characteristics that can match extended job descriptions.

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System and Method for Personalized Search Criteria

When performing web-based searches for employment opportunities, users must spend a lot of time creating a good starting query using specific terms to find the needed job postings. For example, to effectively search for a new job, a user should review the job descriptions one-by-one, and spend a lot of time during the process. Another problem is that, during the review process, the user can become confused by the abundance of information and overlook good job opportunities .

Existing technologies can take user profiles from other sites, such as social media, to provide general input on the search engine; however, these lack an understanding of the specific user and do not always provide accurate search results .

Data analysis applied to the search to understand the user’s characteristics can provide more personalized and accurate search results.

The novel contribution to knowledge is a method and system to analyze and identify a user’s comments , hobbies, preferences, etc. as part of the search key to refine the criteria on search engines and employment websites . The method uses cognitive analysis to identify comments, images, preferences, etc. from the user’s social media or other similar profiles that can be used as input to refine the initial search criteria.

The final output of the system is a list of unconventional job recommendations for users that takes into account the analysis of learned user’s preferences, additional capabilities.

Figure: Process flow


Referring to the above figure, the components and steps for implementing the novel system are : 1. Access internet browser. Access internet from mobile device or a personal computer (PC) using a browser 2. Allow plug-in. Allow plug in to access personal data 3. System Data Extraction. The system extracts common data (e.g., name, gender, ID, etc.) 4. Cognitive Application Programming Interface (API) for data. The system uses cognitive APIs to identify the different social

media pages, profiles, groups, communities that the user follows. 5. Cognitive API reviews comments. The system uses a cognitive API to review comments posted by the user to analyze and