An Improved Data Aggregation Strategy for Group Recommendations

Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in group. Group recommendations can assist a group of users in finding and selecting interesting items thereby considering the tastes of all group members. Traditionally, group recommendations are generated either by aggregating the group members’ recommendations into a list of group recommendations or by aggregating the group members’ preferences (as expressed by ratings) into a group model, which is then used to calculate group recommendations. This paper presents a new data aggregation strategy for generating group recommendations by combining the two existing aggregation strategies. The proposed aggregation strategy outperforms each individual strategy for different sizes of the group and in combination with various recommendation algorithms.