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by Graham Williams
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Group Recommendations

Group recommendation provides recommendations for a group or community rather than an individual. Examples include group study, friends watchig videos together and families travelling.

Recommendations can be provided to small user groups or to much larger online sharing communities.

A typical approach for large group recommendation identifies group interests and connections between users within groups. Different interest subgroups are identified through closely connected users with similar interests. Collaborative filtering can then be used for the subgroups. These recommendations are then aggregated in some way to the provide the group recommendations.

See Qin et al. (2020).

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Other online resources include the GNU/Linux Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
Popular open source software includes rattle and wajig.
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