Enhancing Books Recommendation Systems Via External Information Based on Sentiment Analysis Factors
Keywords:
E-book, book recommender, Sentimental analysis, Recommendation system, Natural language processingAbstract
Obtaining books or scientific research on a specific topic has become increasingly difficult due to the huge amount of material available on the Internet. In this age of information overload, traditional recommender systems alone are no longer enough. They often rely on the number of ratings and reviews a book has received, which may not accurately measure its actual scientific value, leading to recommendations that may inevitably waste the user's valuable time and effort.
To solve this critical issue, it is necessary to develop highly efficient recommendation systems that can provide satisfactory results and to achieve this, incorporating external information from sentiment analysis systems holds great promise. By harnessing the power of sentiment analysis, we can extract the true sentiment and value of a book.
By incorporating sentiment analysis into recommendation systems, we can improve their performance on multiple fronts. First, through sentiment analysis, we can determine not only the amount of feedback writers receive, but also the quality of the feedback. This comprehensive understanding helps filter out irrelevant or biased opinions, ensuring that recommendations are based on reliable and trustworthy sources but are also culturally relevant and contextually appropriate.
Our research aims to contribute a better understanding of the use of sentiment analysis for recommender systems in real-world scenarios. By exploring the potential of sentiment analysis and incorporating its insights, we strive to bridge the gap between traditional recommender systems and user expectations. Ultimately, our efforts seek to improve the user experience and practicality of book recommendations, enabling individuals to effortlessly discover valuable scholarly literature. Through this endeavour, we hope to provide users with reliable and targeted recommendations that enhance their quest for knowledge and expand the horizons of scientific exploration.
