Abstract
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.
Highlights
Wine has been produced for thousands of years, and its earliest recorded history extends back nearly 6000 years ago
We explore the effects of an ontology in a recommendation system
This section presents the results of the experiments that were made on the datasets described above. The objective of these experiments was to assess the impact of the ontology on accuracy classification via clustering
Summary
Wine has been produced for thousands of years, and its earliest recorded history extends back nearly 6000 years ago Since it has become one of the most popular types of alcoholic drinks in the world. Despite being an alcoholic drink that, when consumed in excess, can deteriorate a person’s health, when consumed with responsibility and control, it even has some health benefits [1] With such a variety in types, uses and applications, and with its popularization due to the World Wide Web, the wine market is being slowly shifted to an e-commerce business model. Ontologies are used to solve classification, annotation and rendering, and to create different interpretations that make knowledge representation more effective [4] This is the core relationship between an ontology and a recommendation system and our motivation to build a wine ontology
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