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The book describes a review of a new product recommender system that uses natural language processing (NLP) and text mining to analyze customer reviews. This system, designed to enhance product recommendations, processes reviews from platforms like Amazon and eBay to categorize and rank products based on user queries. It clusters products according to features mentioned in reviews and ranks them based on sentiment (positive or negative). The proposed system, PR-CT, was tested against existing systems using metrics such as Precision, Recall, F1 Score, and Average Response Time, and was found to perform better. However, the abstract notes that more data and development are needed to improve the system's efficiency.
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The book describes a review of a new product recommender system that uses natural language processing (NLP) and text mining to analyze customer reviews. This system, designed to enhance product recommendations, processes reviews from platforms like Amazon and eBay to categorize and rank products based on user queries. It clusters products according to features mentioned in reviews and ranks them based on sentiment (positive or negative). The proposed system, PR-CT, was tested against existing systems using metrics such as Precision, Recall, F1 Score, and Average Response Time, and was found to perform better. However, the abstract notes that more data and development are needed to improve the system's efficiency.