图 书 馆
基于Wasserstein的序列匹配的市场篮筐个性化购买预测Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching |
|
| 课程网址: | http://videolectures.net/kdd2019_kraus_feuerriegel_prediction/ |
| 主讲教师: | Mathias Kraus |
| 开课单位: | 苏黎世联邦理工学院 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, ie, the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction, so that repeat purchases or co-purchases can be identified. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. If desired, this can further be interpreted as a proxy to the prediction quality. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0. This contributes to a further personalization in the provision of retail services. The actual use cases are widespread and include making customers tailored offerings in marketing, extending recommender systems for the purpose of suggesting personalized market baskets, and triggering product deliveries before purchase in order to accelerate delivery. |
| 关 键 词: | 个性化检测; 算法优化; 算法应用 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-03-18：hqh |
| 最后编审: | 2022-03-18：hqh |
| 阅读次数: | 66 |