| Summary: | With ties among people have been much more closer, making recommendations for groups of users became a more general demand, which facilitates the prevalence of group recommender system (GRS). Existing solutions for GRS are mostly established based on preference feedbacks of absolute form such as ratings, yet neglecting that preference assessment criteria are usually heterogeneous among different members. In this paper, we propose GRS-PR, an enhanced group recommender system by exploiting preference relation. First, a preference relation-based multi-variate extreme learning machine model is formulated to predict unknown preference relations in candidate items. Second, on the basis of predicted results, borda voting rule is employed to generate recommendation results from candidate items. In addition, efficiency, parameter sensitivity, and sparsity tolerance of the GRS-PR are evaluated through a set of experiments.
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