Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence. Recently, adaptations of language models, particularly Transformer-based models such as SASRec and BERT4Rec, have achieved state-of-the-art results in sequential recommendation. In these models, item ids replace tokens in the original language models. However, this approach has limitations. First, the vocabulary of item ids may be many times larger than in language models. Second, the classical Top-K recommendation approach used by these models may not be optimal for complex recommendation objectives, including auxiliary objectives such as diversity, coverage or coherence. Recent progress in generative language models inspires us to revisit generative approaches to address these challenges. This paper presents the GPTRec sequential recommendation model, which is based on the GPT-2 architecture. GPTRec can address large vocabulary issues by splitting item ids into sub-id tokens using a novel SVD Tokenisation algorithm based on quantised item embeddings from an SVD decomposition of the user-item interaction matrix. The paper also presents a novel Next-K recommendation strategy, which generates recommendations item-by-item, considering already recommended items. The Next-K strategy can be used for producing complex interdependent recommendation lists. We experiment with GPTRec on the MovieLens-1M dataset and show that using sub-item tokenisation GPTRec can match the quality of SASRec while reducing the embedding table by 40%. We also show that the recommendations generated by GPTRec on MovieLens-1M using the Next-K recommendation strategy match the quality of SASRec in terms of NDCG@10, meaning that the model can serve as a strong starting point for future research.

翻译：序列推荐是一项重要的推荐任务，旨在预测序列中的下一个项目。近年来，语言模型的改编，尤其是基于Transformer的模型（如SASRec和BERT4Rec），在序列推荐中取得了最先进的成果。在这些模型中，项目ID替代了原始语言模型中的标记。然而，这种方法存在局限性。首先，项目ID的词汇表可能比语言模型中的词汇表大许多倍。其次，这些模型使用的经典Top-K推荐方法可能不适用于复杂的推荐目标，包括多样性、覆盖率或连贯性等辅助目标。生成式语言模型的最新进展启发我们重新审视生成式方法以应对这些挑战。本文提出了基于GPT-2架构的GPTRec序列推荐模型。GPTRec通过使用一种新颖的SVD令牌化算法（该算法基于用户-项目交互矩阵SVD分解后的量化项目嵌入）将项目ID拆分为子ID令牌，从而解决大词汇表问题。本文还提出了一种新颖的Next-K推荐策略，该策略逐个生成推荐项目，同时考虑已推荐的项目。Next-K策略可用于生成复杂且相互依赖的推荐列表。我们在MovieLens-1M数据集上对GPTRec进行了实验，结果表明使用子项目令牌化后，GPTRec能够匹配SASRec的质量，同时将嵌入表大小减少40%。我们还表明，在MovieLens-1M数据集上，使用Next-K推荐策略生成的GPTRec推荐在NDCG@10指标上匹配SASRec的质量，这意味着该模型可作为未来研究的强有力起点。