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The variety of interaction paradigms on the Web, such as clicking, commenting or rating are important sources that help recommender systems to gather accurate information about users’ preferences. Ensemble methods can be used to combine all these pieces of information in a post-processing step to generate recommendations that are more relevant. In this paper, we review the application of existing ensemble methods to improve ranking recommendations in the multimodal interactions context. We compared four ensemble strategies, ranging from simple to complex algorithms including Gradient Descent and Genetic Algorithm to find optimal weights. The evaluation using the HetRec 2011 MovieLens 2k dataset with three different types of interactions shows that a considerable 7% improvement in the Mean Average Precision can be achieved using ensembles when compared to the most performant single interaction.

Evaluating Multiple User Interactions for Ranking Personalization Using Ensemble Methods