An evolutionary approach for combining results of recommender systems techniques based on Collaborative Filtering

Recommendation systems work as a counselor, behaving in such a way to guide people in the discovery of products of interest. There are various techniques and approaches in the literature that enable generating recommendations. This is interesting because it emphasizes the diversity of options; on the other hand, it can cause doubt to the system designer about which is the best technique to use. Each of these approaches has particularities and depends on the context to be applied. Thus, the decision to choose among techniques become complex to be done manually. This article proposes an evolutionary approach for combining results of recommendation techniques in order to automate the choice of techniques and get fewer errors in recommendations. To evaluate the proposal, experiments were performed with a dataset from MovieLens and some of Collaborative Filtering techniques. The results show that the combining methodology proposed in this paper performs better than any one of collaborative filtering technique separately in the context addressed. The improvement varies from 9.02% to 48.21% depending on the technique and the experiment executed.