  Recent state-of-the-art recommender systems predominantly rely on either
implicit or explicit feedback from users to suggest new items. While effective
in recommending novel options, many recommender systems often use
uninterpretable embeddings to represent user preferences. This lack of
transparency not only limits user understanding of why certain items are
suggested but also reduces the user's ability to scrutinize and modify their
preferences, thereby affecting their ability to receive a list of preferred
recommendations. Given the recent advances in Large Language Models (LLMs), we
investigate how a properly crafted prompt can be used to summarize a user's
preferences from past reviews and recommend items based only on language-based
preferences. In particular, we study how LLMs can be prompted to generate a
natural language (NL) user profile that holistically describe a user's
preferences. These NL profiles can then be leveraged to fine-tune a LLM using
only NL profiles to make transparent and scrutable recommendations.
Furthermore, we validate the scrutability of our user profile-based recommender
by investigating the impact on recommendation changes after editing NL user
profiles. According to our evaluations of the model's rating prediction
performance on two benchmarking rating prediction datasets, we observe that
this novel approach maintains a performance level on par with established
recommender systems in a warm-start setting. With a systematic analysis into
the effect of updating user profiles and system prompts, we show the advantage
of our approach in easier adjustment of user preferences and a greater autonomy
over users' received recommendations.
