Neural Multi-task Recommendation from Multi-behavior Data
2019 IEEE 35th International Conference on Data Engineering (ICDE)(2019)
摘要
Most existing recommender systems leverage user behavior data of one type, such as the purchase behavior data in E-commerce. We argue that other types of user behavior data also provide valuable signal, such as views, clicks, and so on. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). We perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on the real-world dataset demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data.
更多查看译文
关键词
Predictive models,Business process re-engineering,Training,Recommender systems,Task analysis,Collaboration,Neural networks
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络