A Multi-Dimensional Source Selection Based on Topic Modelling

Access to information in multisource environments is facing many problems. One of them is the source selection problem. As more and more sources become available on the internet, how to select the relevant sources that meet the user needs is a big challenge. In this paper, we propose a multi-dimensional source selection approach based on topic modelling, which integrates both the social dimension and the intelligent dimension in order to optimize the source selection according to different user interests. Social tagging data is analyzed to discover relevant topics of user interests and latent relationships between users and sources based on topic modelling. By intelligently exploring a large search space of possible solutions, an (optimal) selection of sources is found using an intelligent method (a genetic algorithm). The proposed approach is evaluated on real data sources. The experimental results demonstrate that the proposed approach outperforms state-of-the-art source selection algorithms.