Joint search by social and spatial proximity
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This document proposes a new query type called social and spatial ranking query (SSRQ) that incorporates both social and spatial proximity to recommend users to a query user. It develops highly scalable algorithms to process this query type and evaluates them using real social network data. The proposed system ranks users based on both their social distance and spatial distance from the query user, providing a more personalized recommendation than only considering geographic proximity. Hardware requirements include a Pentium IV 2.4 GHz system with 40GB hard disk and 256MB RAM, while the software requires Windows XP, a JSP/ASP.NET front end, and a SQL Server back end.
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Joint search by social and spatial proximity
- 1. Joint Search by Social and Spatial Proximity Abstract: The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and enhance them with elaborate optimizations. Finally, we use real social network data to empirically verify the efficiency and efficacy of our solutions. Existing System: Existing systems apply a traditional k-nearest neighbor query, potentially with some binary conditions (regarding age, sex, etc), to provide u1 with the profiles of users in the vicinity. While recommended users are indeed
- 2. near u1 geographically, his/her true preferences of companions would be better captured if SN information was also. Proposed System: In this paper we propose and study the social and spatial ranking query (SSRQ). SSRQ reports the top-k users in the SN based on a ranking function that incorporates social and spatial distance from the query user. We conduct the first study on a joint search by social and spatial user proximity. We propose a suite of processing methodologies, including a highly scalable and robust approach that relies on indexing and social summaries. Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour. • Mouse : Logitech. • RAM : 256 Mb.
- 3. Software Requirements: • Operating system : - Windows XP. • Front End : - JSP • Back End : - SQL Server Software Requirements: • Operating system : - Windows XP. • Front End : - .Net • Back End : - SQL Server