This paper presents the solution designed by the team "Boston Team Party"for the ACM RecSys Challenge 2022. The competition was organized by Dressipi and was framed under the session-based fashion recommendations domain. Particularly, the task was to predict the purchased item at the end of each anonymous session. Our proposed two-stage solution is effective, lightweight, and scalable. First, it leverages the expertise of several strong recommendation models to produce a pool of candidate items. Then, a Gradient-Boosting Decision Tree model aggregates these candidates alongside several hand-crafted features to produce the final ranking. Our model achieved a score of 0.18800 in the public leaderboard. To aid in the reproducibility of our findings, we open-source our materials.
Lightweight Model for Session-Based Recommender Systems with Seasonality Information in the Fashion Domain
Mainetti L.;Pala R.;Sammarco F.;Perez Maurera F. B.;Bernardis C.;Ferrari Dacrema M.
2022-01-01
Abstract
This paper presents the solution designed by the team "Boston Team Party"for the ACM RecSys Challenge 2022. The competition was organized by Dressipi and was framed under the session-based fashion recommendations domain. Particularly, the task was to predict the purchased item at the end of each anonymous session. Our proposed two-stage solution is effective, lightweight, and scalable. First, it leverages the expertise of several strong recommendation models to produce a pool of candidate items. Then, a Gradient-Boosting Decision Tree model aggregates these candidates alongside several hand-crafted features to produce the final ranking. Our model achieved a score of 0.18800 in the public leaderboard. To aid in the reproducibility of our findings, we open-source our materials.File | Dimensione | Formato | |
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