Predicting Click-Through Rates is a crucial function within recommendation and advertising platforms, as the output of CTR prediction determines the order of items shown to users. The Embedding and MLP paradigm has become a standard approach for industrial recommendation systems and has been widely deployed. However, this paradigm suffers from cold-start problems, where there is either no or only limited user action data available, leading to poorly learned ID embeddings. The cold-start problem hampers the performance of new items. To address this problem, we design a novel diffusion model to generate a warmed-up embedding for new items. Specifically, we define a novel diffusion process between the ID embedding space and the side information space. In addition, we can derive a sub-sequence from the diffusion steps to expedite training, given that our diffusion model is non-Markovian. Our diffusion model is supervised by both the variational inference and binary cross-entropy objectives, enabling it to generate warmed-up embeddings for items in both the cold-start and warm-up phases. Additionally, we have conducted extensive experiments on three recommendation datasets. The results confirmed the effectiveness of our approach.

Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs).To enhance the reasoning performance of LLMs, diverse Reinforcement Learning (RL)-based fine-tuning approaches have been proposed to address the limited generalization capability of LLMs trained solely via Supervised Fine-Tuning (SFT).Despite their effectiveness, two major limitations hinder the advancement of LLMs.First, vanilla RL-based approaches ignore annotated Chainof-Thought (CoT) and incorporate unstable reasoning path sampling, which typically results in model collapse, unstable training process, and suboptimal performance.Second, existing SFT approaches generally overemphasize the annotated CoT, potentially leading to performance degradation due to insufficient exploitation of potential CoT.In this paper, we propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach, i.e., CARFT, to enhance the reasoning performance of LLMs while addressing the aforementioned limitations.Specifically, we propose learning a representation for each CoT.Based on this representation, we design novel contrastive signals to guide the fine-tuning process.Our approach not only fully exploits the available annotated CoT but also stabilizes the finetuning procedure by incorporating an additional unsupervised learning signal.We conduct comprehensive experiments and in-depth analysis with three baseline approaches, two foundation models, and two datasets to demonstrate significant advantages of CARFT in terms of robustness, performance (up to 10.15%), and efficiency (up to 30.62%).Code is available at https://github.com/WNQzhu/CARFT.

Rotary Position Embedding (RoPE) is an efficient position encoding approach and is widely utilized in numerous large language models (LLMs).Recently, a lot of methods have been put forward to further expand the context window based on RoPE.The core concept of those methods is to predefine or search for a set of factors to rescale the base frequencies of RoPE.Nevertheless, it is quite a challenge for existing methods to predefine an optimal factor due to the exponential search space.In view of this, we introduce PSC (Phase Shift Calibration), a small module for calibrating the frequencies predefined by existing methods.With the employment of PSC, we demonstrate that many existing methods can be further enhanced, like PI, YaRN, and LongRoPE.We conducted extensive experiments across multiple models and tasks.The results demonstrate that (1) when PSC is enabled, the comparative reductions in perplexity increase as the context window size is varied from 16k, to 32k, and up to 64k.(2) Our approach is broadly applicable and exhibits robustness across a variety of models and tasks.The code can be found at https://github.com/WNQzhu/PSC.

One of the challenging problems in large-scale recommendation systems is to retrieve relevant candidates accurately and efficiently. Graph-based retrievals have been widely deployed in industrial recommendation systems. Previous graph-based methods depend on integrated graph infrastructures because of inherent data dependency in graph learning. However, it could be expensive to develop a graph infrastructure. In this paper, we present a simple and effective graph-based retrieval method, which does not need any graph infrastructures. We conduct extensive offline evaluations and online tests in a real-world recommendation system. The results show that the proposed method outperforms the existing methods. The source code of our algorithm is available online.