Large Language Models (LLMs) offer groundbreaking advancements in recommender systems through superior text analysis and decision-making support. However, integrating LLMs into recommender systems still suffers from the problems of identifier uninterpretability and lack of transparency. To address these issues and fully leverage the capabilities of LLMs, we propose a chain of thought (CoT) based recommendation framework called CoT4Rec which employs LLMs as data enhancers for user preference analysis. Initially, we design a CoT reasoning strategy that can derive more behaviorally-aligned user preference features by clustering users’ historical interactions. Subsequently, we propose a two-stage recommendation model that not only makes full use of the world knowledge embedded in LLMs but also generates a logically transparent reasoning path. By integrating a user preference analyzer early in the recommendation pipeline, the model deeply analyzes users' historical interactions, helping to enhance the personalization and transparency of the recommender system. CoT4Rec demonstrates superior performance over existing state-of-the-art models in recommendation tasks across four public datasets, achieving improvements ranging from 2.2% to 12.2%.

The increasing parameter size of Deep Neural Networks (DNNs) has significantly enhanced model performance. As large-scale DNN models typically require partitioning into multiple blocks for distributed training, existing research has predominantly focused on offline scheduling for individual or batched training tasks. However, the stochastic arrival of such tasks in edge networks poses a critical challenge for efficiently scheduling them in resource-constrained edge clusters. In this paper, aiming to minimize the average training completion time across all tasks, we first extract DNN operator graphs and partition them into coarse-grained subgraphs using a max-flow mincut algorithm. Then, we formulate the online scheduling problem for multiple distributed DNN training tasks as a Markov Decision Process (MDP) and propose a reinforcement learning-based (RL-based) solution. Extensive experiments comparing our method with three conventional baselines (FIFO, SJF, and Greedy) under diverse configurations show that our approach reduces the average training completion time by 12.95%, demonstrating its effectiveness in resource-constrained edge environments with dynamic workloads.