Parallel context-aware multi-agent tourism recommender system

The presence of millions and millions of users and items makes real time filtering a time consuming process in recommender systems (RS). In context aware recommender systems (CARS), choices of users depend on the contextual information as well as available items. The rapid change in interests of a user under different contexts puts extra load on RS. To address this problem, we present a parallel approach for CARS using a multi-agent system which accelerates the processing time drastically using General Purpose Graphic Processing Unit (GPGPU). Contextual pre-filtering and multi-agent environment update the system with the user context. A prototype of the system is developed using JCuda, JADE and Java technologies for tourism domain. The performance of the presented system is compared with the CARS without parallel processing with respect to processing time, scalability as well as precision, recall and F-measure. The results show a significant speedup for presented system over non-parallel CARS.