标题： 生成式多目标跨领域推荐 显示英文标题

标题： Generative Multi-Target Cross-Domain Recommendation

摘要： 最近，多目标跨域推荐（MTCDR）引起了广泛关注，其旨在同时提升多个领域的推荐性能。现有的MTCDR方法主要依赖于领域共享实体（\eg 用户或物品）来融合和迁移跨领域知识，这在非重叠推荐场景中可能不可用。一些研究将用户偏好和物品特征建模为领域共享的语义表示，这些表示可用于解决MTCDR任务。然而，它们通常需要大量的辅助数据进行预训练。开发更有效的MTCDR解决方案仍然是一个重要的研究领域。受生成式推荐最新进展的启发，本文引入了GMC，这是一种基于生成范式的多目标跨域推荐方法。GMC的核心思想是利用语义量化离散物品标识符作为统一生成模型中集成多领域知识的媒介。GMC首先使用物品分词器为每个物品生成领域共享的语义标识符，然后通过训练一个领域统一的序列到序列模型，将物品推荐建模为下一个标记生成任务。为了进一步利用领域信息以提高性能，我们在语义标识符学习中引入了一个领域感知对比损失，并在统一推荐器上进行领域特定的微调。在五个公开数据集上的大量实验表明，与多种基线方法相比，GMC表现出色。 显示英文摘要

摘要： Recently, there has been a surge of interest in Multi-Target Cross-Domain Recommendation (MTCDR), which aims to enhance recommendation performance across multiple domains simultaneously. Existing MTCDR methods primarily rely on domain-shared entities (\eg users or items) to fuse and transfer cross-domain knowledge, which may be unavailable in non-overlapped recommendation scenarios. Some studies model user preferences and item features as domain-sharable semantic representations, which can be utilized to tackle the MTCDR task. Nevertheless, they often require extensive auxiliary data for pre-training. Developing more effective solutions for MTCDR remains an important area for further exploration. Inspired by recent advancements in generative recommendation, this paper introduces GMC, a generative paradigm-based approach for multi-target cross-domain recommendation. The core idea of GMC is to leverage semantically quantized discrete item identifiers as a medium for integrating multi-domain knowledge within a unified generative model. GMC first employs an item tokenizer to generate domain-shared semantic identifiers for each item, and then formulates item recommendation as a next-token generation task by training a domain-unified sequence-to-sequence model. To further leverage the domain information to enhance performance, we incorporate a domain-aware contrastive loss into the semantic identifier learning, and perform domain-specific fine-tuning on the unified recommender. Extensive experiments on five public datasets demonstrate the effectiveness of GMC compared to a range of baseline methods.