Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
By Judith Masthoff. User modeling and user-adapted interaction, 2004
501 citations recorded by the source corpus.
Subfields: Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/92f0b4baad34e7515f0687c4de9f1b9d85243dbc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Sociology and Political Science
- Anthony David Jameson
- Nava Tintarev
- Jie Lu
- Mingsong Mao
- Wei Wang
- Dianshuang Wu
- Guangquan Zhang
- Barry Smyth
In Statistical and Nonlinear Physics
- Anthony David Jameson
- Nava Tintarev
- Jie Lu
- Guangquan Zhang
- Mingsong Mao
- Wei Wang
- Dianshuang Wu
- Barry Smyth
Citations in the corpus
- Watching television over an IP network (2008, background): “(See [35] for a discussion of how group decisions are taken when watching television.”
- Group Recommender Systems: Combining Individual Models (2011, methodology): “In contrast, some evaluation of YU'S TV RECOMMENDER has taken place [29].”
- Group recommendations with rank aggregation and collaborative filtering (2010, background): “Masthoff [11] addressed the related problem of how subjects aggregate recommendation lists, i.”
- Evaluating the effectiveness of explanations for recommender systems (2012, background): “This is for example the case in a group recommender system, where users may have to compromise given varying tastes in the group (Masthoff 2004 ).”
- Recommendation to Groups (2007, background): “But often the group members will expect a system to make recommendations concerning a larger set of decisions, either at the same time or in succession: I NTRIGUE ’s tour guide will choose several sights to visit; recommender will recommend several programs for a group to watch in succession.”
- More than the sum of its members: challenges for group recommender systems (2004, background): “, [3] and [11] for further relevant contributions).”
- Recommender system application developments: A survey (2015, methodology): “…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 strategy; and Popescu [71] adopted the voting mechanism.”
- Recommender Systems (2016, unknown): “Group recommender systems are discussed in detail in [271, 272, 407, 408].”