Predicting Group Choices from Group Profiles
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Group recommender systems (GRS) identify items to recommend to a group by aggregating group members' individual preferences into a group profile. The preference aggregation strategy used to build the group profile can also be used for predicting the item that a group may decide to choose, i.e., by assuming that the group is applying exactly that strategy. However, predicting the choice of a group is challenging since the RS is not aware of the precise preference aggregation strategy that is going to be used by the group. Hence, the aim of this paper is to validate the research hypothesis that, by using a machine learning approach and a data set of observed group choices, it is possible to predict a group's final choice, better than by using a standard preference aggregation strategy. Inspired by Social Decision Scheme theory, which first tried to address the group choice prediction problem, we search for a group profile definition that, in conjunction with a machine learning model, can be used to accurately predict a group choice. Moreover, to cope with the data scarcity problem, we propose two data augmentation methods, which add synthetic group profiles to the training data, and we hypothesise they can further improve the choice prediction accuracy. We validate our research hypotheses by using a data set containing 282 participants organized in 79 groups. The experiments indicate that the proposed methods outperform baseline aggregation strategies when used for group choice prediction. The proposed method is robust with the presence of missing preference data and achieves a performance superior to what human can achieve on the group choice prediction task. Finally, the proposed data augmentation method can also improve the prediction accuracy. Our approach can be exploited in novel GRSs to identify the items that the group is likely to choose and help the group to make a better choice.
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