soMLier: a South African wine recommender system

Abstract

ABSTRACT Recommender Systems (RS) are used to generate recommendations of items that a user may be interested in. Several commercial wine recommender systems exist but are largely tailored to consumers outside of South Africa (SA). Consequently, these systems are of limited use to novice wine consumers in SA. In this research, a system soMLier (a combination of the terms ‘sommelier’ and ‘Machine Learning’) is developed for SA consumers that yields high-quality wine recommendations, maximises the accuracy of predicted ratings for those recommendations and provides insights into why those suggestions were made. This system is developed using two datasets – a database containing several attributes of SA wines and the corresponding numeric 5-star ratings made by users on Vivino.com. Using these datasets, several recommendation methodologies are investigated and it is found that collaborative filtering succeeds at generating lists of relevant wine recommendations, matrix factorisation techniques accurately predict ratings and content-based methods are most appropriate for explaining wine recommendations. These methods are optimally combined in the soMLier system. Though it would benefit from more explicit user data to establish a richer model of user preferences, soMLier can assist consumers in discovering wines they will likely enjoy and understanding their preferences of SA wine. Abbreviations: SA: South Africa(n); RS: Recommender System(s); IBCF: Item-basedCollaborative Filtering; CB: Content-Based; MF: Matrix Factorisation; RMSE: RootMean Square Error; COV: Coverage; PER: Personalistion; ARHR: Average ReciporcalHit Rate