RecSys_Dataset: Beauty Product reviews dataset for sentiment analysis and recommendation system

Product reviews help the sellers to understand their customers' expectations and sentiment towards the product and based on those reviews they take measures accordingly to heighten the satisfaction level of their customers. Beauty products are unique because various factors can influence a customer's purchase decision. With the help of machine learning techniques, the product reviews can be utilized to achieve insights and patterns to understand customers sentiment and recommend products according to their purchase records. To maintain the confidentiality of user, real dataset was not used. A synthetic dataset can heighten the efficiency of machine learning techniques. This dataset was generated by AI, packs a vast number of reviews of various products, sentiment towards those and elaborate exploratory analysis. A total of 50,000 reviews were generated from 200 different products and 1,000 unique users. A series of processing steps were performed on the raw dataset, including content addition. Another aspect of this work is that there are still not many datasets available that contains user CTR (Click-Through Rate) alongside their Spent time on a product interface which can help the researchers exploit this dataset to develop recommender systems, and natural language processing algorithms for analytical purposes.