U-Search: usage-based search with collective intelligence. (2017) - Yin, Pengfei; Wang, Guojun; Jiang, Wenjun
With the emergence of big data in the networking environments, searching the most suitable information for users is getting more challenging. We propose a usage-based search model, U-Search, to retrieve high quality resources which meet user's requirements. There are mainly two processes in our model, first-stage retrieval (FRet) and second-stage retrieval (SRet). At FRet, general users collect useful resources from a variety of channels and import them into the resource sharing platform after the resource checking. At SRet, a user inputs his search purpose firstly. Then, our model will match the purpose in the platform by judging the relevance between user profile and resource profile. Finally, the result list will be generated by integrating the usage of influential users and the calculated relevance. Experiment on the offline dataset shows that our model can truly provide the suitable search result to the user.