A Restaurant Recommendation Engine Using Feature-based Explainable Matrix Factorization
Mohammed Alshammari · 2022
Black box algorithms have demonstrated their superiority in handling the sparse data that the modern Internet is saturated with. Transparency, which is essential for building trust in recommender systems, is missing, nevertheless. Collaborative Filtering techniques require lots of items ratings for the model to function properly, moreover, item-side information greatly aids in creating interpretations because filtering models frequently rely on ratings. In our study, we suggest a new approach for producing justifications while maintaining high accuracy. The findings of the research demonstrate that our suggested solution performs better in terms of accuracy and transparency.