  We primarily focus on the field of large language models (LLMs) for
recommendation, which has been actively explored recently and poses a
significant challenge in effectively enhancing recommender systems with logical
reasoning abilities and open-world knowledge. Current mainstream efforts mainly
center around injecting personalized information from recommendation models
into LLMs by customizing input templates or aligning representations between
semantic and recommendation spaces at the prediction layer. However, they face
three significant limitations: (1) LoRA is mostly used as a core component in
existing works, but personalization is not well established in LoRA parameters
as the LoRA matrix shared by every user may not cater to different users'
characteristics, leading to suboptimal performance. (2) Although lifelong
personalized behavior sequences are ideal for personalization, their use raises
effectiveness and efficiency issues since LLMs require escalating training and
inference time to extend text lengths. (3) Existing approaches aren't scalable
for large datasets due to training efficiency constraints. Thus, LLMs only see
a small fraction of the datasets (e.g., less than 10%) instead of the whole
datasets, limiting their exposure to the full training space. To address these
problems, we propose RecLoRA. This model incorporates a Personalized LoRA
module that maintains independent LoRAs for different users and a Long-Short
Modality Retriever that retrieves different history lengths for different
modalities, significantly improving performance while adding minimal time cost.
Furthermore, we design a Few2Many Learning Strategy, using a conventional
recommendation model as a lens to magnify small training spaces to full spaces.
Extensive experiments on public datasets demonstrate the efficacy of our
RecLoRA compared to existing baseline models.
