Integration of Linked Open Data in Collaborative Group Recommender Systems

A group recommender system (GRS) is a system that collectively recommends items to a group of users based on their preferences. The GRS and the individual RS challenge lies in a very small and incompleteness of user-item ratings. Such incompleteness resulted in the data sparsity problem. The issues of data sparsity in a group negatively affect the quality of recommendations to the group. It occurs due to the inefficient formation of groups, which usually involves individuals with sparse data in their user profiles. Most of the current studies focus on this issue after the formation of groups. However, this study focused before the group formation, based on the intuition that it will be more efficient if the data sparsity at the individual level is addressed before the group formation process takes place. Therefore, applying the approach through Linked Open Data (LOD) technology is proposed to ensure that the data sparsity issues can be overcome before the group formation process is implemented. We proposed a GRS-LOD model. The experimental evaluations relating to the prediction accuracy and recommendation relevancy of the proposed model were implemented on three aspects: comparison with the basic approach or baselines; comparison with the current approaches, and comparison in terms of group size and aggregation strategies. The aggregation strategies used were the Average (AV), Most Pleasure (MP), Average without Misery (AVM), and Least Misery (LM). The metrics for prediction accuracy were based on the RMSE and MAE, whereas for relevancy, precision, recall, and F1-score were considered. The results show that the prediction accuracy and relevancy of the developed model's recommendations is better than the baseline study by adapting the Average (AV) strategy with the individual profile aggregation approach. Meanwhile, for the evaluation in terms of group size, the results show larger group size exhibits better prediction accuracy for the four used aggregation strategies. On the other hand, in terms of recommendation relevancy, the result shows that relevancy decreases with the increase in group size for the MP, AV and AVM strategies.