Controllable Multi-Interest Framework for Recommendation
By Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, Jie Tang. Knowledge Discovery and Data Mining, 2020
384 citations recorded by the source corpus.
Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequential recommendation problem, intending to predict the next items that the user might be interacted with. Recent works usually give an overall embedding from a user's behavior sequence. However, a unified user embedding cannot reflect the user's multiple interests during a period. In this paper, we propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our multi-interest module captures multiple interests from user behavior sequences, which can be exploited for retrieving candidate items from the large-scale item pool. These items are then fed into an aggregation module to obtain the overall recommendation. The aggregation module leverages a controllable factor to balance the recommendation accuracy and diversity. We conduct experiments for the sequential recommendation on two real-world datasets, Amazon and Taobao. Experimental results demonstrate that our framework achieves significant improvements over state-of-the-art models. Our framework has also been successfully deployed on the offline Alibaba distributed cloud platform.
Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/054c5e48d41fcadfd4dbc19a48089d87c9814a4f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xiangnan He
- Tat-seng Chua
- Julian McAuley
- Xiang Wang
- Maarten de Rijke
- Wenqiang Lei
- Yongfeng Zhang
- Caiming Xiong
In Information Systems
- Xiangnan He
- Tat-seng Chua
- Xiang Wang
- Julian McAuley
- Wenqiang Lei
- Maarten de Rijke
- Chongming Gao
- Jia Li
In Management Science and Operations Research
- Tat-seng Chua
- Xiangnan He
- Wenqiang Lei
- Maarten de Rijke
- Zhiwei Liu
- Jia Li
- Caiming Xiong
- Yongjun Chen
Citations in the corpus
- Intent Contrastive Learning for Sequential Recommendation (2022, methodology): “Thus we follow [3, 13, 51] to use a sampled softmax technique to randomly sample a negative item for each time step in each sequence.”
- Advances and Challenges in Conversational Recommender Systems: A Survey (2021, background): “There are many factors affecting a user’s decisions in real life [109, 17, 49].”