AutoMLP: Automated MLP for Sequential Recommendations

Abstract

Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.

arXiv commentAccepted by WWW'23

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• Neural Machine Translation by Jointly Learning to Align and Translate
• MLP-Mixer: An all-MLP Architecture for Vision
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• ResMLP: Feedforward networks for image classification with data-efficient training
• MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation

Cited by in corpus (7)

• LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems
• HAMUR: Hyper Adapter for Multi-Domain Recommendation
• FineRec:Exploring Fine-grained Sequential Recommendation
• Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
• Efficient and Robust Regularized Federated Recommendation
• Modeling User Retention through Generative Flow Networks
• Generative Archetype-Grounded Item Representations for Sequential Recommendation