  Large Language Models (LLMs) have demonstrated remarkable capabilities and
have been extensively deployed across various domains, including recommender
systems. Prior research has employed specialized \textit{prompts} to leverage
the in-context learning capabilities of LLMs for recommendation purposes. More
recent studies have utilized instruction tuning techniques to align LLMs with
human preferences, promising more effective recommendations. However, existing
methods suffer from several limitations. The full potential of LLMs is not
fully elicited due to low-quality tuning data and the overlooked integration of
conventional recommender signals. Furthermore, LLMs may generate inconsistent
responses for different ranking tasks in the recommendation, potentially
leading to unreliable results.
  In this paper, we introduce \textbf{RecRanker}, tailored for instruction
tuning LLMs to serve as the \textbf{Ranker} for top-\textit{k}
\textbf{Rec}ommendations. Specifically, we introduce an adaptive sampling
module for sampling high-quality, representative, and diverse training data. To
enhance the prompt, we introduce a position shifting strategy to mitigate
position bias and augment the prompt with auxiliary information from
conventional recommendation models, thereby enriching the contextual
understanding of the LLM. Subsequently, we utilize the sampled data to assemble
an instruction-tuning dataset with the augmented prompts comprising three
distinct ranking tasks: pointwise, pairwise, and listwise rankings. We further
propose a hybrid ranking method to enhance the model performance by ensembling
these ranking tasks. Our empirical evaluations demonstrate the effectiveness of
our proposed RecRanker in both direct and sequential recommendation scenarios.
