Prompt Tuning Large Language Models on Personalized Aspect Extraction
  for Recommendations
Pan Li Yuyan Wang Ed H. Chi Minmin Chen
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.