Abstract
Recently, recommender systems have been popularly used to handle massive data collected from applications such as movies, music, news, books, and research articles in a very efficient way. In practice, users generally prefer to take other people’s opinions before buying or using any product. A rating is a numerical ranking of items based on a parallel estimation of their quality, standards, and performance. Ratings do not elaborate many things about the product. On the contrary, reviews are formal text evaluation of products where reviewers freely mention pros and cons. Reviews are more important as they provide insight and help in making informed decisions. Today the internet works as an exceptional originator of consumer reviews. The amount of opinionated data is increasing speedily, which is making it impractical for users to read all reviews to come to a conclusion. The proposed approach uses opinion mining which analyzes reviews and extracts different products features. Every user does not have the same preference for every feature. Some users prefer one feature, while some go for other features of the product. The proposed approach finds users’ inclination toward different features of products and based on that analysis it recommends products to users.
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Tewari, A.S., Jain, R., Singh, J.P., Barman, A.G. (2019). Personalized Product Recommendation Using Aspect-Based Opinion Mining of Reviews. In: Chakraborty, M., Chakrabarti, S., Balas, V., Mandal, J. (eds) Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing, vol 811. Springer, Singapore. https://doi.org/10.1007/978-981-13-1544-2_36
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