Controllable Multi-Interest Framework for Recommendation

Citations Over TimeTop 1% of 2020 papers

Abstract

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.

Related Papers

• → Recommender systems: models, challenges and opportunities(2023)5 cited
• → Big Data Analysis using Apache Hadoop and Spark(2019)5 cited
• → Big Data 4.0: The Era of Big Intelligence(2024)4 cited
• → Hybrid-based Research Article Recommender System(2023)3 cited
• → Introduction to Bigdata and Relation with IoT(2018)