Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models; (2) interpreting user interests to improve relevance and diversity; and (3) adapting practical circumstances such as growing item inventories. To address these limitations, we present GPT4Rec, a novel and flexible generative framework inspired by search engines. It first generates hypothetical "search queries" given item titles in a user's history, and then retrieves items for recommendation by searching these queries. The framework overcomes previous limitations by learning both user and item embeddings in the language space. To well-capture user interests with different aspects and granularity for improving relevance and diversity, we propose a multi-query generation technique with beam search. The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items. With GPT-2 language model and BM25 search engine, our framework outperforms state-of-the-art methods by $75.7\%$ and $22.2\%$ in Recall@K on two public datasets. Experiments further revealed that multi-query generation with beam search improves both the diversity of retrieved items and the coverage of a user's multi-interests. The adaptiveness and interpretability of generated queries are discussed with qualitative case studies.

翻译：近期自然语言处理领域的进展催生了基于NLP的推荐系统，展现出优越性能。然而，现有模型通常将物品视为简单ID并采用判别式建模，导致以下局限：（1）未能充分利用物品内容信息及NLP模型的语言建模能力；（2）缺乏对用户兴趣的解释能力以提升推荐相关性与多样性；（3）难以适应物品库规模增长等实际场景。为解决上述问题，我们提出GPT4Rec——一个受搜索引擎启发的灵活生成式框架。该框架首先基于用户历史行为中的物品标题生成假设性"搜索查询"，继而通过检索这些查询获取推荐物品。通过将用户与物品嵌入统一至语言空间，该框架克服了前述局限。针对用户兴趣的多维度与多粒度特性，我们提出基于束搜索的多查询生成技术以提升推荐相关性与多样性。生成的查询天然可作为用户兴趣的可解释性表征，且可通过检索实现冷启动物品推荐。基于GPT-2语言模型与BM25搜索引擎，本框架在两个公开数据集上Recall@K指标分别超越现有最优方法75.7%和22.2%。实验进一步表明，结合束搜索的多查询生成技术可有效提升检索物品多样性及用户多兴趣覆盖率。最后通过定性案例研究，探讨了生成查询的适应性与可解释性。