Context aware filtering using social behavior of frogs

The problem of information overload surfaced with the emergent popularity of dynamic web applications. To tackle this issue, various context awareness approaches have been developed to filter the information. Conventional context aware social filtering techniques predominantly focus on time and location as context of the users. However, another relevant context that of user׳s demographic information is often left out. The paper presents demographic context based filtering using social behavior of frogs. The approach employs shuffled frog leaping algorithm (SFLA) to perform the context modeling and handle the sparsity and scalability issues in social filtering. The work proposes two distinct methodologies to model the demographic context – SFLA based Contextual two dimensional (SC2D) and SFLA based Contextual three dimensional (SC3D) approach. SC2D approach primarily develops a model based on social behavior and subsequently incorporates the personal demographic (occupation, gender, etc.) context to compute the most relevant items. In the SC3D approach, personal demographic context is amalgamated with social behavior to develop the model and thereafter a contextual model is used to generate most relevant items. Experimental studies revealed that SC2D approach is able to reduce the error up to 15% and 8% as compared to MAC2D and GAC2D, respectively, and SC3D approach improves the accuracy upto 31% with respect to MAC3D and upto 26% as compared to GAC3D. Analysis of variance (ANOVA) test results for all approaches corroborate that the differences between the means of SC2D, MAC2D and GAC2D and SC3D, MAC3D and GAC3D are highly significant. These results improve confidence in SFLA as a better optimization algorithm for context aware filtering.