Conversational recommender systems (CRS) use interactive discussions to recommend high-quality items to users. Two essential components in a good CRS are the recommendation module that makes pertinent product recommendations to consumers and a conversation component that creates text-based sentences with product recommendations. The most commonly used dataset to train CRS models is ReDial. In this paper, we found that using the INSPIRED dataset in place of the ReDial dataset significantly improves model performance in terms of effectiveness. Along with the INSPIRED dataset, the inclusion of historical data in the input improves efficiency. The accuracy and efficiency of the model increase when we include the historical data into the system in the form of DialoGPT corpus and Gutenberg books. The paper further extends to compare three versions of state-of-the-art knowledge graph based conversational recommender systems called KGSF â one with the INSPIRED dataset with history, one with the INSPIRED dataset without historical data and the last with the ReDial dataset without historical data, which is the original version of the KGSF model. The comparison between three versions of the KGSF model shows that the change of the dataset and the inclusion of historical data can promote the performance of this conversational recommendation system.