A Completely Locale-independent Session-based Recommender System by Leveraging Trained Model

Kei HARADA

doi:10.48550/arXiv.2310.07281

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ジャーナル論文 - rm_misc: Others

A Completely Locale-independent Session-based Recommender System by Leveraging Trained Model

Kei HARADA

CoRR, Vol.abs/2310.07281

11/10/2023

DOI: https://doi.org/10.48550/arXiv.2310.07281

抄録

In this paper, we propose a solution that won the 10th prize in the KDD Cup 2023 Challenge Task 2 (Next Product Recommendation for Underrepresented Languages/Locales). Our approach involves two steps: (i) Identify candidate item sets based on co-visitation, and (ii) Re-ranking the items using LightGBM with locale-independent features, including session-based features and product similarity. The experiment demonstrated that the locale-independent model performed consistently well across different test locales, and performed even better when incorporating data from other locales into the training.

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https://doi.org/10.48550/arXiv.2310.07281表示

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https://dblp.uni-trier.de/rec/journals/corr/abs-2310-07281表示

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http://arxiv.org/abs/arXiv:2310.07281表示

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https://dblp.uni-trier.de/db/journals/corr/corr2310.html#abs-2310-07281表示

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詳細

タイトル: A Completely Locale-independent Session-based Recommender System by Leveraging Trained Model
作成者 – 役職なし: Kei HARADA
出版物に関する詳細: CoRR, Vol.abs/2310.07281
ID: 991002560176207421
組織: The University of Electro-Communications
資料タイプ: ジャーナル論文
リソースのサブタイプ: rm_misc: Others