Abstract
Explainable recommendation system (ERS) in addition to recommending items to the user also explains why the recommendation is being made. Explanations improve user acceptance and system transparency. Since every recommender system (RS) has some strength and weakness, a combination of RS may be required to demonstrate both performance and explainability. In this paper, an innovative framework is proposed for systematic evaluation of different configuration of ERS compared with respect to performance and explainability of RS recommendations. Framework uses a novel approach to configure RS with different types of recommender models, hybridization of recommender models to create new models, well-defined metrics to compare performance and explainability of recommendation given by ERS. Simulation experiments show the efficacy of the framework in helping users gain insight into how various components of ERS effect explainability and recommendation quality.
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Mukherjee, N., Karthik, G.M. (2021). Framework for Evaluation of Explainable Recommender System. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_11
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DOI: https://doi.org/10.1007/978-981-15-8767-2_11
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