Toward a Transparent Recommender System
Transparent Recommender Systems, especially those based on machine learning, could convey the reasoning behind their predictions of great importance. In the literature, many Recommender Systems based on Machine Learning have proven their strength. However, focusing on improving accuracy in these systems means more complex algorithms (black box models) making the reasons behind the prediction difficult to understand. Explainability and efficiency should not be at odds with each other. The few existing neural networks based recommendation algorithms suffer from data sparsity problems caused by the lack of rated items, or user social information is not exploited. Recently, Knowledge Graphs have been explored for that purpose. However, still, manual efforts are required for feature extraction. Graph Embeddingbased algorithms are promising solutions that use the Graph Theory properties to learn user and item feature vectors for generating the recommendation. The goal of this paper is to outline the research questions set out in my Ph.D. thesis related to the transparency and trust in a recommendation by providing an intuitive explanation and to put forward the preliminary ideas to tackle the challenges.
RuleML+RR