Group Recommendation: Semantics and Efficiency

     This paper discusses the problem that is apparent in group recommendation, creating a recommendation based on a group's overall tastes. This paper covers a summary of different methods for single users and talks about extending these functions to work for groups of users.

     The first approach is score aggregation. This method uses different scores obtained from analysis of single users and uses it to create a list of suggestions for the group. They do this by average and least misery to hopefully better predict the group's tastes. The other method used is the threshold algorithm. This gives you an implementation for the top k query processing using a variation of the common Threshold Algorithm.

     Both of these approaches could be used in our final implementation. Once we get a system that works on one algorithm, then we can keep adding more and more till it gets the best suggestion.


http://dl.acm.org/citation.cfm?id=2063576.2063839