Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit user feedback such as user reviews. It however remains under-explored how the extracted aspects can help generate more meaningful recommendations to the users. Meanwhile, existing research on aspect-based recommendations often relies on separate aspect extraction models or assumes the aspects are given, without accounting for the fact the optimal set of aspects could be dependent on the recommendation task at hand. In this work, we propose to combine aspect extraction together with aspect-based recommendations in an end-to-end manner, achieving the two goals together in a single framework. For the aspect extraction component, we leverage the recent advances in large language models and design a new prompt learning mechanism to generate aspects for the end recommendation task. For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model. The recommendation task mediates the learning of the user embeddings and item embeddings, which are used as soft prompts to generate aspects. Therefore, the extracted aspects are personalized and contextualized by the recommendation task. We showcase the effectiveness of our proposed method through extensive experiments on three industrial datasets, where our proposed framework significantly outperforms state-of-the-art baselines in both the personalized aspect extraction and aspect-based recommendation tasks. In particular, we demonstrate that it is necessary and beneficial to combine the learning of aspect extraction and aspect-based recommendation together. We also conduct extensive ablation studies to understand the contribution of each design component in our framework.

翻译：现有方面抽取方法大多依赖于显式或真实方面信息，或采用数据挖掘与机器学习方法从用户隐性反馈（如用户评论）中抽取方面。然而，关于抽取的方面如何助力生成更有意义的用户推荐这一议题仍待深入探索。同时，现有基于方面的推荐研究通常依赖独立的方面抽取模型或假设方面已给定，未考虑最优方面集可能取决于具体推荐任务这一事实。本研究提出将方面抽取与基于方面的推荐以端到端方式结合，在统一框架中同时实现两个目标。在方面抽取组件中，我们利用大语言模型的最新进展，设计了一种新颖的提示学习机制，为最终推荐任务生成方面。在基于方面的推荐组件中，抽取的方面与推荐模型常用的用户和物品特征拼接。推荐任务通过中介学习用户嵌入与物品嵌入，这些嵌入被用作软提示来生成方面。因此，所抽取的方面通过推荐任务实现个性化与情境化。我们在三个工业数据集上通过大量实验展示了所提方法的有效性，所提框架在个性化方面抽取与基于方面的推荐任务中均显著优于最先进的基线模型。特别是，我们证明了将方面抽取与基于方面推荐的学习过程相结合具有必要性与优越性。我们还通过广泛消融实验深入理解框架中各设计组件的贡献。