Group Recommendation: Semantics and Efficiency

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search (2013, background): “The problem of group recommendation [3] is applicable in many more settings such as recommending people to join circles, etc.”
Make it personal: A social explanation system applied to group recommendations (2017, methodology): “Hence, in this work, S RM is used as starting point and not other state-of-the-art approaches (Amer-Yahia et al., 2009; Masthoff, 2011) as it was already proved that S RM outperformed non-social approaches (Quijano-Sánchez et al., 2013).”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “…profile by aggregating the user profiles in the group and then make recommendations for the group profile, and the other is to first generate recommendations for all users in the group and then aggregate the results as the final output for the group [Amer-Yahia et al. 2009; Campos et al. 2009].”
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering (unknown): “Note that our primary goal is to recommend items for a single user, not for a group of users [Amer-Yahia et al. , 2009].”