Exploiting Multiple Recommenders to Improve Group Recommendation

Abstract

Group recommendation methods deal with scenarios where a group is the target of recommendation instead of a single user. An initial approach followed by these methods was to aggregate the rankings generated to each individual user of the group by traditional recommender systems. This approach was replaced to more sophisticated methods, but the potential and simplicity of the aggregation strategies were underexplored. This paper proposes to use multiple recommenders to generate recommendations to single group members before aggregating their recommendations. We show that this strategy significantly improves the results of aggregating single recommenders while overcoming the problem of selecting the best recommendation algorithm. We also propose five heuristics to select a subset of the available recommenders to be aggregated. We tested heuristics in seven dataset variations, showing that by using half of the available algorithms we can achieve results similar or better than those obtained by the whole set.