01/09/2022
Περίληψη
Product recommendation is considered a well-known technique for bringing customers and products together. With applications in
...
music, electronic shops, or almost any platform the user daily deals with, the
recommendation system’s sole scope is to help customers and attract new ones to
discover new products. Through product recommendation, transaction costs can also
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 in choosing the appropriate recommendation method and
drawing 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 this transactional data, concluding
that LightFM provides more efficient recommendation results following the evaluation’s
precision and recall outcomes. Nevertheless, even though the Surprise library outperforms,
it should be used when constructing user-friendly models, requiring low code and low
technicalities.
Συγγραφείς
Agori Argyro Patoulia, Athanasios Kiourtis, Argyro Mavrogiorgou, Dimosthenis Kyriazis