Detecting wine taste using Recommender Systems

Wine comes in a wide variety of types and has many different flavours. Of all human senses, taste is often seen as the most personal of all. It is unique and helps determine what food to eat or what drink to consume. Consequently, complex beverages as wine are not perceived in the same manner by everyone. Getting an understanding of wine and of one’s pallet can be difficult and very time-consuming. This paper investigates the ability of a recommender system to detect taste and whether it can give wine suggestions correlative to taste preferences. This paper also explores the data that is required to create a wine recommender system and considers how this data is collected. In addition, this research addresses a language interpretation problem. The results of the recommendation techniques used during this research gave useful insights into the dataset required for such a recommender system and allow future possibilities for this approach to be explored.