Wine Ontology Influence in a Recommendation System

• Published in: Big data and cognitive computing Vol. 5; no. 2; p. 16
• Main Authors: Oliveira, Luís, Rocha Silva, Rodrigo, Bernardino, Jorge
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2021
• Subjects: Alcoholic beverages; Algorithms; Beer; Classification; classification via clustering; Clustering; Collaboration; Data mining; Datasets; Electronic commerce; Knowledge; Machine learning; Ontology; ontology influence; Preferences; Product reviews; recommendation system; Recommender systems; Weka clustering algorithms; wine ontology
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc5020016

Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will recommend a product. The metadata consist mostly of former reviews or web traffic from the same user. For this reason, we investigate what would happen if the information analyzed by a recommendation system was insufficient. In this paper, we explore the effects of a new wine ontology in a recommendation system. We created our own wine ontology and then made two sets of tests for each dataset. In both sets of tests, we applied four machine learning clustering algorithms that had the objective of predicting if a user recommends a wine product. The only difference between each set of tests is the attributes contained in the dataset. In the first set of tests, the datasets were influenced by the ontology, and in the second set, the only information about a wine product is its name. We compared the two test sets’ results and observed that there was a significant increase in classification accuracy when using a dataset with the proposed ontology. We demonstrate the general applicability of the methodology to other cases, applying our proposal to an Amazon product review dataset.