2 0 0 0 OA 人工衛星の夜間光画像を用いた製造業の業種別の生産活動分析
- 著者
- 水門 善之 福馬 智生
- 出版者
- 一般社団法人 人工知能学会
- 雑誌
- 人工知能学会研究会資料 知識ベースシステム研究会 117回 (2019/8) (ISSN:24364592)
- 巻号頁・発行日
- pp.05, 2019-08-01 (Released:2021-07-14)
1 0 0 0 OA 協調フィルタリングにおけるメタラーニングの適用による疎なデータからの学習と不確実性の推論
- 著者
- 福馬 智生 鳥海 不二夫
- 出版者
- 一般社団法人 人工知能学会
- 雑誌
- 人工知能学会論文誌 (ISSN:13460714)
- 巻号頁・発行日
- vol.35, no.5, pp.F-JC3_1-9, 2020-09-01 (Released:2020-09-01)
- 参考文献数
- 27
Latent factor models such as Matrix Factorization have become the default choice for recommender systems due to their performance and scalability. However, such algorithms have two disadvantages. First, these models suffer from data sparsity. Second, they fail to account for model uncertainty. In this paper, we exploit a meta learning strategy to address these problems. The key idea behind our method is to learn predictive distributions conditioned on context sets of arbitrary size of user/item interaction information. Our proposed framework has the advantages of being easy to implement and applicable to any existing latent factor models, providing uncertainty capabilities. We demonstrate the significant superior performance of our model over previous state-of-the-art methods, especially for sparse data in the top-N recommendation task.