SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation

2026-06-17

AI Summary

This work addresses key limitations in existing intent-aware recommender systems, which typically rely on a predefined number of intents, are sensitive to behavioral sequence quality, and lack explicit semantic grounding, resulting in coarse-grained intent representations. To overcome these issues, the authors propose a novel approach that leverages sparse autoencoders to unsupervisedly disentangle fine-grained, interpretable intent spaces from text embeddings generated by large language models, eliminating the need to predefine the number of intents. The method distinguishes between user-specific personalized intents and cross-user common intents and introduces a multi-branch attention mechanism that adaptively integrates temporal dynamics with intent priors. Extensive experiments on multiple public datasets demonstrate significant performance gains over state-of-the-art baselines, while also yielding human-interpretable explanations for recommendations.

Abstract

Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors. Most existing models derive intents directly from user sequences via clustering or prototype learning. However, they are sensitive to sequence quality, require presetting the number of intents, and lack explicit semantic grounding. These issues lead to an incomplete and coarse intent set and limit the effectiveness of recommendation. In this paper, we propose the Sparse Autoencoder for intent-based recommendation (SAERec), a novel recommender that automatically constructs a fine-grained and interpretable intent space from a textual corpus to guide recommendation. Rather than treating texts as side signals, SAERec leverages them as high information density evidence for intent construction. Specifically, we first extract a comprehensive set of fine-grained interpretable intents from the latent space of large language models (LLMs) by using a sparse autoencoder (SAE) to disentangle and interpret text embeddings, which isolates intent-related semantics from textual noise. Then, for each user, we retrieve relevant intents from this set as priors to guide recommendation. It contains personal intents matching a user's current interests and public intents capturing general item patterns shared across users (e.g., quality, price). Finally, to integrate retrieved intents into sequence modeling, we propose a multi-branch attention mechanism that captures temporal dependencies and injects both personal and public intent signals, followed by an adaptive fusion layer to construct the final user representation for recommendation. Extensive experiments on public datasets demonstrate the superiority of SAERec, consistently outperforming state-of-the-art baselines while providing human-understandable explanations.

Problem

• intent-based recommendation
• interpretable intents
• sparse autoencoder
• semantic grounding
• user behavior modeling

Innovation

• Sparse Autoencoder
• Intent-based Recommendation
• Interpretable AI
• Large Language Models
• Multi-branch Attention