<![CDATA[The era of abundant information and the continuous introduction of new products and services has made it increasingly challenging for users to navigate numerous options. Recommender systems have emerged as essential tools to help users find personalized and relevant information quickly. This paper proposes a hybrid recommender system that effectively processes online customer reviews using word embedding and clustering techniques. The system generates product-feature words, detects sentiment words and their intensity, analyzes word correlations, and extracts variables from the reviews for the product. Word embedding models, such as Word2Vec, are employed to capture the semantic content of product reviews and descriptions. The attributes extracted from the text data and word embeddings are combined to create a hybrid representation of products. Based on this hybrid representation, the system calculates the similarity among products using cosine similarity and other measures. Finally, it returns a ranked list of recommended best products based on how similar they are to either an inputted product or user preferences. We have implemented the system and experimental evaluations have been carried out on the “Datafiniti Electronics Product Data" dataset. We aim to provide personalized recommendations to users based on online reviews, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age. The developed prototype will provide personalized recommendations to users, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age.]]>
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