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for groups. Therefore, it is advantageous to cluster the user for a group based on their features. The more similar
the user preferences are in the group, the better the group recommendations (Pessemier et al., 2014).

4. Conclusion

This work discusses the issue of sparsity in the context of GRS and applies the proposed model, GRS-LOD,
in the group formation process applying LOD technology. The acquired findings demonstrate the richness of
the data retrieved from DBpedia can enhance group formation. The experimental findings further prove that the
proposed method may generate acceptable and quality group recommendations. Thus, we believe this work
represents a preliminary step towards a new generation of LOD-enabled GRS as it can be explored to other new
dimensions in GRS. In future work, we will investigate the context of explaining the recommendation for groups
since it facilitates group members to grasp the system's recommended items.

Acknowledgements

The authors gratefully acknowledge the sponsorship received to carry out this study from Tun Hussein
Onn University of Malaysia and from the Malaysia Ministry of Higher Education.

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