Language Models (LMs) are increasingly employed in recommendation systems due to their advanced language understanding and generation capabilities. Recent recommender systems based on generative retrieval have leveraged the inferential abilities of LMs to directly generate the index tokens of the next item, based on item sequences within the user's interaction history. Previous studies have mostly focused on item indices based solely on textual semantic or collaborative information. However, although the standalone effectiveness of these aspects has been demonstrated, the integration of this information has remained unexplored. Our in-depth analysis finds that there is a significant difference in the knowledge captured by the model from heterogeneous item indices and diverse input prompts, which can have a high potential for complementarity. In this paper, we propose SC-Rec, a unified recommender system that learns diverse preference knowledge from two distinct item indices and multiple prompt templates. Furthermore, SC-Rec adopts a novel reranking strategy that aggregates a set of ranking results, inferred based on different indices and prompts, to achieve the self-consistency of the model. Our empirical evaluation on three real-world datasets demonstrates that SC-Rec considerably outperforms the state-of-the-art methods for sequential recommendation, effectively incorporating complementary knowledge from varied outputs of the model.

翻译：语言模型因其先进的语言理解和生成能力，正越来越多地被应用于推荐系统中。基于生成式检索的近期推荐系统利用语言模型的推理能力，根据用户交互历史中的物品序列，直接生成下一个物品的索引标记。先前的研究大多集中于仅基于文本语义信息或协同信息的物品索引。然而，尽管这些方面各自的有效性已得到证实，但对此类信息的整合仍未得到探索。我们的深入分析发现，模型从异构物品索引和多样化输入提示中捕获的知识存在显著差异，这具有很高的互补潜力。在本文中，我们提出了SC-Rec，一个统一的推荐系统，它从两种不同的物品索引和多个提示模板中学习多样化的偏好知识。此外，SC-Rec采用了一种新颖的重排序策略，该策略聚合了一组基于不同索引和提示推断出的排序结果，以实现模型的自洽性。我们在三个真实世界数据集上的实证评估表明，SC-Rec显著优于序列推荐领域的最先进方法，有效地整合了来自模型多样化输出的互补知识。