Disentangling Long and Short-Term Interests for Recommendation

Abstract

Modeling user's long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may lead to inferior recommendation accuracy and interpretability. In this paper, to address it, we propose a Contrastive learning framework to disentangle Long and Short-term interests for Recommendation (CLSR) with self-supervision. Specifically, we first propose two separate encoders to independently capture user interests of different time scales. We then extract long-term and short-term interests proxies from the interaction sequences, which serve as pseudo labels for user interests. Then pairwise contrastive tasks are designed to supervise the similarity between interest representations and their corresponding interest proxies. Finally, since the importance of long-term and short-term interests is dynamically changing, we propose to adaptively aggregate them through an attention-based network for prediction. We conduct experiments on two large-scale real-world datasets for e-commerce and short-video recommendation. Empirical results show that our CLSR consistently outperforms all state-of-the-art models with significant improvements: GAUC is improved by over 0.01, and NDCG is improved by over 4%. Further counterfactual evaluations demonstrate that stronger disentanglement of long and short-term interests is successfully achieved by CLSR. The code and data are available at https://github.com/tsinghua-fib-lab/CLSR.

Accepted by WWW'22

References in corpus (9)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Bootstrap your own latent: A new approach to self-supervised Learning
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• Disentangled Graph Collaborative Filtering
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
• DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
• Self-Supervised Reinforcement Learning for Recommender Systems

Cited by in corpus (7)

• Disentangling ID and Modality Effects for Session-based Recommendation
• When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
• Augmented Negative Sampling for Collaborative Filtering
• Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
• Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
• CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
• Query-dominant User Interest Network for Large-Scale Search Ranking