Controllable Multi-Interest Framework for Recommendation

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.

Accepted to KDD 2020

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Cited by in corpus (6)

• Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
• Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
• Future-Aware Diverse Trends Framework for Recommendation
• Controllable Recommenders using Deep Generative Models and Disentanglement
• Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)
• MIC: Model-agnostic Integrated Cross-channel Recommenders