Generative recommendation has emerged as a promising paradigm aimed at augmenting recommender systems with recent advancements in generative artificial intelligence. This task has been formulated as a sequence-to-sequence generation process, wherein the input sequence encompasses data pertaining to the user's previously interacted items, and the output sequence denotes the generative identifier for the suggested item. However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals. In this paper, we introduce content-based collaborative generation for recommender systems, denoted as ColaRec. To capture collaborative signals, the generative item identifiers are derived from a pretrained collaborative filtering model, while the user is represented through the aggregation of interacted items' content. Subsequently, the aggregated textual description of items is fed into a language model to encapsulate content information. This integration enables ColaRec to amalgamate collaborative signals and content information within an end-to-end framework. Regarding the alignment, we propose an item indexing task to facilitate the mapping between the content-based semantic space and the interaction-based collaborative space. Additionally, a contrastive loss is introduced to ensure that items with similar collaborative GIDs possess comparable content representations, thereby enhancing alignment. To validate the efficacy of ColaRec, we conduct experiments on three benchmark datasets. Empirical results substantiate the superior performance of ColaRec.

中文翻译：

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通过内容和协作集成增强生成推荐

生成推荐已成为一种有前景的范式，旨在利用生成人工智能的最新进展来增强推荐系统。该任务已被表述为序列到序列的生成过程，其中输入序列包含与用户先前交互的项目有关的数据，并且输出序列表示建议项目的生成标识符。然而，现有的生成推荐方法仍然遇到挑战：（i）在统一的生成框架内有效地集成用户-项目协作信号和项目内容信息，以及（ii）在内容信息和协作信号之间执行有效的对齐。在本文中，我们介绍了基于内容的协作生成推荐系统，表示为 ColaRec。为了捕获协作信号，生成项目标识符是从预先训练的协作过滤模型中导出的，而用户则通过交互项目内容的聚合来表示。随后，项目的聚合文本描述被输入到语言模型中以封装内容信息。这种集成使 ColaRec 能够在端到端框架内合并协作信号和内容信息。关于对齐，我们提出了一个项目索引任务，以促进基于内容的语义空间和基于交互的协作空间之间的映射。此外，还引入了对比损失，以确保具有相似协作 GID 的项目具有可比较的内容表示，从而增强对齐。为了验证 ColaRec 的功效，我们在三个基准数据集上进行了实验。实证结果证实了 ColaRec 的优越性能。