Context-Awareness in Ensemble Recommender System Framework
Ahlem Drif, Houssem Eddine Zerrad, Hocine Cherifi · 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · 2021
Recommender systems that provide recommendations based uniquely on information over users and items may not be very accurate in some situations. Therefore, adding contextual information to recommendations may be a good choice resulting in a system with increased precision. In an early work, we proposed an Ensemble Variational Autoencoders (EnsVAE) framework for recommendation. EnsVAE is adjusted to output interest probabilities by learning the distribution of each item's ratings and attempts to provide diverse novel items that are pertinent to users. In this paper, we propose and investigate a context awareness framework based on the Ensemblist Variational Autoencoders model with integrating the contextual information. The context awareness EnsVAE can easily be inferred from preceding sub-recommenders or applied as a filter to the final output. Test performed on real dataset, using an instance of the proposed framework show clear improvement compared to baseline architectures with similar ends as of this instance.