Emerging Science Journal (ESJ), (in press), Ital Publication

Product recommendation is considered as a well-known technique for bringing customers and products together. With applications in music, electronic shops or almost any platform the user daily deal with, recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, also transaction costs can be decreased, improving overall decision making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor to choosing the appropriate recommendation method and draw specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of these transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though Surprise library outperforms, it should be used when constructing user-friendly models, re-quiring low code and low technicalities