| タイトル | Collaborative Filtering Based on the Latent Class Model for Attributes |
|---|---|
| 著者 | 小林 学 、三河 健太 、後藤 正幸 、松嶋 敏泰 、平澤 茂一 |
| 年度 | 2017 |
| 形式 | 国際学会 |
| 分野 | 知識情報処理 |
| 掲載雑誌名 | Proceedings of IEEE International Conference on Machine Learning and Applications (ICMLA) |
| 掲載号・ページ | 893--896 |
| 掲載年 | 2017 |
| 掲載月 | 12 |
| アブスト （日本語） |
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) 2017年12月18日～21日 Cancun, Mexico 査読有 DOI:10.1109/ICMLA.2017.00-42 https://ieeexplore.ieee.org/document/8260750 |
| アブスト （英語） |
In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior of customers and services with various attributes for marketing. We assume that each customer and service have the invisible attribute which is called latent class. Assuming a combination of attribute values of a customer and service is classified to a latent class, furthermore, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer, service and attribute values. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation. |
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