PolyLens: A recommender system for groups of user

By Mark O'Connor, Dan Cosley, J. Konstan, John Riedl. European Conference on Computer Supported Cooperative Work, 2001

606 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/02078d1d91cbd23ee92b7e876997a6dfc7351154

How researchers see this paper

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

In Computer Science Applications

1. Charu C. Aggarwal
2. Judith Masthoff
3. J. Konstan
4. John Riedl
5. Michael D. Ekstrand
6. Jie Lu
7. Mingsong Mao
8. Wei Wang

In Information Systems

1. Charu C. Aggarwal
2. Judith Masthoff
3. John Riedl
4. J. Konstan
5. Nava Tintarev
6. Jonathan L. Herlocker
7. Francesco Ricci
8. Michael D. Ekstrand

In Information Systems and Management

1. Charu C. Aggarwal
2. Judith Masthoff
3. J. Konstan
4. John Riedl
5. Anthony David Jameson

Citations in the corpus

• Recommender Systems (2016, positive): “Group recommendations have been used in a variety of domains, such as movies [168], television [653], music [412], and travel [52, 272, 413].”
• Group Recommender Systems: Combining Individual Models (2011, background): “POLYLENS [7] Proposes movies for a group to view Yes No No No Least Misery”
• Group Recommendation: Semantics and Efficiency (2009, methodology): “Furthermore, with the exception of PolyLens [13], only few conducted user studies to evaluate the benefits of group recommendations.”
• Group recommendations with rank aggregation and collaborative filtering (2010, background): “Group RSs have been designed to work in different domains: web/news pages [17], tourism [15, 13], music [6, 14], TV programs and movies [16, 21].”
• Collaborative Filtering Recommender Systems (2011, methodology): “group’s recommendations by merging the individual predictions using the “principle of least misery”: the predicted preference for a movie was the lowest preference predicted for any group member [107].”
• Recommender systems: from algorithms to user experience (2012, background): “Rashid et al. (2002) explored six algorithms for soliciting ratings from new users.”
• Collaborative Filtering Recommender Systems (2007, background): “CF can help groups of people find items that maximize value to group as a whole [46].”
• Make it personal: A social explanation system applied to group recommendations (2017, background): “This goes along with most existing group recommender systems, that regardless of their domain, ignore the social factors that influence real group decision-making (O’Connor et al., 2001; McCarthy, 2002).”
• More than the sum of its members: challenges for group recommender systems (2004, background): “But as research so far has shown, the transition from an individual user to a group of users requires much more than an aggregation function.”
• Seamful interweaving: heterogeneity in the theory and design of interactive systems (2004, background): “heterogeneously with regard to users), but collective recommendations, drawing from all users’ histories and presented homogenously, are feasible [28].”
• 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…”