ユーザ感想フィードバックを活用した推薦システムの提案

抄録

In the wake of the COVID-19 pandemic, the inclination towards online shopping has grown, further highlighting the importance of recommendation systems in e-commerce. Traditional recommendation algorithms require extensive datasets to perform effectively, which poses a challenge for small to medium-sized enterprises with limited data. This study proposes a recommendation system that significantly reflects user preferences, even with constrained data volumes, to enhance user satisfaction. We conducted computational experiments simulating actual products and typical users to validate the effectiveness of our approach. Our results, presented through a series of rankings and user preference data, demonstrate the system's ability to adapt to user feedback and improve recommendation quality. Future work will focus on extracting specific reasons from product evaluations to refine the rating of similar items automatically and re-construct user ratings based on the similarity of preferences across a database.