SPAR: Personalized Content-Based Recommendation via Long Engagement Attention
This work addresses the problem of insufficient user-item interaction and long text processing in content-based recommendation systems, offering an incremental improvement for users and platforms.
The paper tackles the challenge of extracting holistic user interests from long engagement histories for personalized content recommendations, and introduces SPAR, a framework that outperforms state-of-the-art methods on two benchmark datasets.
Leveraging users' long engagement histories is essential for personalized content recommendations. The success of pretrained language models (PLMs) in NLP has led to their use in encoding user histories and candidate items, framing content recommendations as textual semantic matching tasks. However, existing works still struggle with processing very long user historical text and insufficient user-item interaction. In this paper, we introduce a content-based recommendation framework, SPAR, which effectively tackles the challenges of holistic user interest extraction from the long user engagement history. It achieves so by leveraging PLM, poly-attention layers and attention sparsity mechanisms to encode user's history in a session-based manner. The user and item side features are sufficiently fused for engagement prediction while maintaining standalone representations for both sides, which is efficient for practical model deployment. Moreover, we enhance user profiling by exploiting large language model (LLM) to extract global interests from user engagement history. Extensive experiments on two benchmark datasets demonstrate that our framework outperforms existing state-of-the-art (SoTA) methods.