Group Recommender Systems: Combining Individual Models

By Judith Masthoff. Recommender Systems Handbook, 2011

394 citations recorded by the source corpus.

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Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “A simple model could take the group-level preference to be the intersection of all members’ individual preferences [Masthoff 2011].”
Multistakeholder recommendation: Survey and research directions (2020, background): “Group recommendation has a substantial history within recommender systems research as surveyed in Masthoff (2011).”
Recommender Systems (2016, neutral): “A variety of aggregation strategies for group recommender systems, such as plurality voting, multiplicative aggregation, Borda count, Copeland rule, approval voting, and fairness are suggested in [407].”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “Other advanced recommendation techniques include context-aware (CA) (Adomavicius and Tuzhilin 2011; Huang et al. 2011b), trust-aware based (TA), fuzzy-based (FB) (Zhang et al. 2013b), social network-based (SB) (He and Chu 2010), group-based (GB) (Masthoff 2011) and ontology-based techniques.”
Recommender system application developments: A survey (2015, methodology): “Masthoff [12] summarized eleven strategies including least misery, average, most pleasure and their adaptations, as the most common in GRS. Quijano-Sanchez et al. [68] used average strategy; PolyLens [69] used the least misery strategy [70]; MusicFX used a variant of the average without misery…”