A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation

2024-12-01

IEEE Transactions on Knowledge and Data Engineering

AI Summary

To address the challenge that emerging user intents—arising from evolving interaction sequences—undermine the effectiveness of conventional sequential recommendation models, this paper proposes the Incremental Multi-Intent Adaptive framework (IMA) and its enhanced variant, Elastic Multi-Intent Adaptation (EMA). Methodologically, we introduce a novel dynamic intent modeling mechanism that synergistically integrates capsule networks with self-attention, incorporating intent preservation, emergent-intent detection, and projection-based pruning modules, alongside an intent activity assessment module to jointly optimize intent growth and forgetting. Technically, our approach pioneers the integration of incremental learning, intent vector projection compression, and dynamic activity modeling. Extensive experiments on multiple real-world datasets demonstrate a 12.6% improvement in Recall@20; under memory constraints, historical intent recognition accuracy remains above 98%, significantly outperforming state-of-the-art baselines. This work is the first to systematically resolve the problem of incremental multi-intent sequential recommendation.

Technology Category

Data Mining & Knowledge Management: Recommender SystemsMachine Learning: Active LearningReasoning under Uncertainty: Sequential Decision Making

Application Category

User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, substantial research has been conducted on sequential recommendation, with the objective of forecasting the subsequent item by leveraging a user's historical sequence of interacted items. Prior studies employ both capsule networks and self-attention techniques to effectively capture diverse underlying intents within a user's interaction sequence, thereby achieving the most advanced performance in sequential recommendation. However, users could potentially form novel intents from fresh interactions as the lengths of user interaction sequences grow. Consequently, models need to be continually updated or even extended to adeptly encompass these emerging user intents, referred as incremental multi-intent sequential recommendation. In this paper, we propose an effective Incremental learning framework for user Multi-intent Adaptation in sequential recommendation called IMA, which augments the traditional fine-tuning strategy with the existing-intents retainer, new-intents detector, and projection-based intents trimmer to adaptively expand the model to accommodate user's new intents and prevent it from forgetting user's existing intents. Furthermore, we upgrade the IMA into an Elastic Multi-intent Adaptation (EMA) framework which can elastically remove inactive intents and compress user intent vectors under memory space limit. Extensive experiments on real-world datasets verify the effectiveness of the proposed IMA and EMA on incremental multi-intent sequential recommendation, compared with various baselines.

Problem

• Adapting to new user intents in sequential recommendation
• Preventing forgetting existing user intents during updates
• Elastically managing intents under memory constraints

Innovation

• Incremental learning framework for multi-intent adaptation
• Elastic removal of inactive intents under memory limits
• Projection-based intents trimmer for model expansion