More than the sum of its members: challenges for group recommender systems

By Anthony David Jameson. International Working Conference on Advanced Visual Interfaces, 2004

299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/11e7f243a31edb072695b5738380f4055d3510e9

How researchers see this paper

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

In Artificial Intelligence

1. Judith Masthoff
2. Barry Smyth
3. John Riedl
4. J. Konstan
5. Michael D. Ekstrand
6. Jie Lu
7. Xingshe Zhou
8. Zhiwen Yu

In Signal Processing

1. Judith Masthoff
2. Barry Smyth
3. Michael D. Ekstrand
4. John Riedl
5. Xingshe Zhou
6. Zhiwen Yu
7. Jianhua Gu
8. J. Konstan

Citations in the corpus

• Collaborative Filtering Recommender Systems (2011, background): “Jameson [66] describes a number of challenges in building group recommenders.”
• Recommender Systems (2016, unknown): “However, the consensus over the years has evolved into designing recommender systems, which are better than the sum of their parts and can take the interactions between the various users into account for designing recommendations [272, 413].”
• Make it personal: A social explanation system applied to group recommendations (2017, background): “Although some explanation components have been included in group recommender systems (Jameson, 2004; McCarthy et al., 2006; Boratto and Carta, 2011) none of them have focused on using the social reality within a group for explanation generation.”
• TV Program Recommendation for Multiple Viewers Based on user Profile Merging (2006, methodology): “To aggregate a group of users’ interests, TRAVEL DECISION FORUM (Jameson 2004) provides an interface for explicit preferences input and allows each member to see the other member’s preferences.”
• Group Recommender Systems: Combining Individual Models (2011, background): “The issue of manipulability is most relevant when users provide explicit ratings, used for group recommendation only, and are aware of others' ratings, all of which is the case in the TRAVEL DECISION FORUM.”
• Recommendation to Groups (2007, methodology): “) Similarly, explicit preference specifications are required by the TRAVEL DECISION FORUM (Jameson [18]; Jameson et al.”
• Recommender system application developments: A survey (2015, methodology): “DCOP-based multiagent e-group Agent Web-based group 2008 [169] TDF e-group Asynchronous Web-based group 2004 [72, 170] FIT e-group CB TV-based group 2004 [171] TV4M e-group CB TV-based group 2006 [172] TV programme recommender e-group Classifier TV-based group 2009 [173]”