SPAR: Personalized Content-Based Recommendation via Long Engagement Attention

arXiv:2402.10555v1 Announce Type: cross
Abstract: 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 …

abstract arxiv attention cs.cl cs.ir encoding engagement language language models nlp personalized processing recommendation recommendations semantic struggle success tasks textual type via