• Kumar, Sunil
• Kotecha, K.
• Singh, Pardeep
• Bathla, Gourav
• Garg, Deepak
• Verma, Madhushi

Abstract

<jats:title>Abstract</jats:title><jats:p>In the futuristic Industry framework, user interactions with the product are seamlessly integrated with the product life cycle. A recommender system can be considered as an information filtering tool that provides suggestions to users about products, music, friend, topic, etc. This suggestion is based on the interest of users. Several research works have been carried out to improve recommendation accuracy by using matrix factorization, trust-based, hybrid-based, machine learning, and deep learning techniques. However, very few existing works have leveraged textual opinions for the recommendation to the best of our knowledge. Existing research works have focused only on numerical ratings, which do not reflect actual user behaviour. In this research work, sentiments of textual opinions are analyzed for an in-depth analysis of users' behaviour. Furthermore, Natural Language Processing techniques such as lemmatization, stemming, stop-word removal, Part-of-Speech (POS) tagging are applied to textual opinions. Recommendation accuracy is improved by using the proposed score Recop calculated from opinion sentiments. Furthermore, the sparsity issue is resolved by using our proposed approach. Amazon and Yelp review datasets are used for Experiment analysis. Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) values are improved significantly using the proposed approach compared to the existing approaches. MAE and RMSE scores on the Yelp dataset are <jats:italic>0.85</jats:italic> and <jats:italic>1.51</jats:italic>, respectively. Additionally, MAE and RMSE scores on the Amazon dataset are <jats:italic>0.66</jats:italic> and <jats:italic>0.93</jats:italic>, respectively, significantly contributing to our proposed approach.</jats:p>

Topics

• impedance spectroscopy
• experiment
• machine learning
• microwave-assisted extraction