The integration of large language models (LLMs) into recommender systems has garnered attention due to their ability to leverage rich semantic representations. However, these models suffer from a limitation, knowledge bias, a systematic tendency to over-recommend items dominated by pretraining knowledge, regardless of actual user interests. Although recent studies have focused on fine-tuning LLMs to better align with the collaborative data, our investigation reveals that such fine-tuning fails to fundamentally correct this deep-seated bias. In this work, we propose ReKnow, a lightweight and efficient decoding-time alignment method to mitigate knowledge bias. Specifically, we quantify LLMs’ knowledge towards biased items, then realign output probabilities to match the target data distribution. For validation, we provide both theoretical justifications and empirical results, including evaluation on two datasets, demonstrating that our  enhances the quality and diversity of LLM-based sequential recommendation. We have release our code[<https://anonymous.4open.science/r/ReKnow-B17E>] for others to reproduce the results.

The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices. To simplify access to predefined AI services, this paper introduces MIRA, a pioneering framework for task instruction recommendation that enables intuitive one-touch AI tasking on smartphones. With MIRA, users can long-press on images or text objects to receive contextually relevant instruction recommendations for executing AI tasks. Our work introduces three key innovations: 1) A multimodal large language model (MLLM)-based recommendation pipeline with structured reasoning to extract key entities, infer user intent, and generate precise instructions; 2) A template-augmented reasoning mechanism that integrates high-level reasoning templates, enhancing task inference accuracy; 3) A prefix-tree-based constrained decoding strategy that restricts outputs to predefined instruction candidates, ensuring coherence and intent alignment. Through evaluation using a real-world annotated datasets and a user study, MIRA has demonstrated substantial improvements in recommendation accuracy. The encouraging results highlight MIRA’s potential to revolutionize the way users engage with AI services on their smartphones, offering a more seamless and efficient experience.