  Recent advancements have showcased the potential of Large Language Models
(LLMs) in executing reasoning tasks, particularly facilitated by
Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve
clear, definitive answers and logical chains of thought, the application of LLM
reasoning in recommendation systems (RecSys) presents a distinct challenge.
RecSys tasks revolve around subjectivity and personalized preferences, an
under-explored domain in utilizing LLMs' reasoning capabilities. Our study
explores several aspects to better understand reasoning for RecSys and
demonstrate how task quality improves by utilizing LLM reasoning in both
zero-shot and finetuning settings. Additionally, we propose RecSAVER
(Recommender Systems Automatic Verification and Evaluation of Reasoning) to
automatically assess the quality of LLM reasoning responses without the
requirement of curated gold references or human raters. We show that our
framework aligns with real human judgment on the coherence and faithfulness of
reasoning responses. Overall, our work shows that incorporating reasoning into
RecSys can improve personalized tasks, paving the way for further advancements
in recommender system methodologies.
