Abstract
With the democratization of the web, recent works aimed at making recommendations for groups of people to consider the circumstances where the item is selected to be consumed collectively. This paper proposes a group recommender system which is able to support partial rankings of items from different users in the form of top-k lists. In fact, the proposed group recommender system is based on generating recommendation lists for the group members using user-based collaborative filtering, then applying approximation Borda rule to generate group recommendations. Experiments show that the proposed group recommender system using approximate voting rules produced more accurate and interesting recommendations than using the standard voting rules.
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Ben Abdrabbah, S., Ayadi, M., Ayachi, R., Ben Amor, N. (2017). Aggregating Top-K Lists in Group Recommendation Using Borda Rule. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_38
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DOI: https://doi.org/10.1007/978-3-319-60042-0_38
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