The dynamic nature of microservice architectures necessitates robust few-shot anomaly detection (AD) systems to prevent cascading failures. However, current multimodal approaches remain inadequate for cold-start scenarios due to their dependence on extensive training data. The fundamental challenge in few-shot AD lies in two critical aspects: (1) effective feature extraction from intra-modalities, and (2) the alignment of heterogeneous multimodal representations, particularly the distribution discrepancy between discrete token embeddings from logs and continuously evolving representations from traces and metrics. To overcome these challenges, we present FuseGuard, an LLM-enhanced framework that pioneers quality-aware multimodal fusion for few-shot AD. First, our intra-modal feature extraction employs a Dynamic Sparse Correlation Attention mechanism for metrics, Spatio-temporal Encoding for traces, and LLM-enhanced semantic-frequency encoding for logs, enabling adaptive representation of time-evolving patterns. Second, we introduce hierarchical cross-modal alignment via token projection, layer-wise distillation, and consistency learning to preserve modality-specific features while enabling effective knowledge transfer. Finally, a quality-aware fusion mechanism dynamically weights modalities based on uncertainty estimation. Evaluated on three open-source platforms (MSDS, GAIA, TrainTicket) and a production system, FuseGuard's few-shot capability outperforms SOTA by 19.69%-77.07% F1-score across four datasets.

Microservices architecture has become increasingly popular in modern software systems, yet its complexity also poses challenges for failure diagnosis. Existing literature has deficiencies in multimodal data mining, mainly in two aspects: first, the mining of multimodal data is insufficient, failing to fully exploit the rich information contained in different types of data; second, the analysis of spatiotemporal features of multimodal data is not thorough, failing to fully explore the potential associations of data in the temporal and spatial dimensions. To address these issues, this paper proposes a new method for anomaly detection in microservices. We transform three types of modal data into the wavelet domain and achieve fusion among modalities based on an attention mechanism, fully utilizing the inherent characteristics of multimodal data in the frequency and temporal domains. This fusion process can extract new information that does not exist in the original modalities, thereby enhancing the model's ability to detect anomalies. In addition, we propose a new diffusion probabilistic model (DDPM) based on spatiotemporal graphs, which combines spatiotemporal learning capabilities with the uncertainty measurement of DDPM to generate future samples in a non-autoregressive manner, achieving multi-horizon prediction. Experimental results show that our method significantly improves anomaly detection performance on three public datasets and a real-world production system, demonstrating its effectiveness in microservices anomaly detection.

E-commerce recommendation platforms often operate across multiple domains, giving rise to Multi-Domain Recommendation (MDR) methods. These methods leverage auxiliary information from other domains to capture comprehensive user preferences and enhance recommendation accuracy. However, cross-domain knowledge transfer often causes domains to capture similar preferences. Additionally, most MDR models optimize for next-item prediction, which promotes greedy strategies focused on immediate gains within individual domains while neglecting the platform-wide profit. These two factors result in vicious competition, as our empirical analysis reveals: Domains recommend overlapping item lists, competing for the same pool of user attention and platform traffic, reducing the overall orders. To address this, we propose the Direct Preference Optimization from Manual Rules (DPOMR) training paradigm for MDR. Inspired by Reinforcement Learning from Human Feedback (RLHF), DPOMR learns user preferences from pairwise preference data generated through manual rules rooted in business expertise. Furthermore, we design a variant of Direct Preference Optimization (DPO) to bypass expensive RL training. Extensive experiments demonstrate the advantages of DPOMR, including: (1) achieving SOTA performance in boosting overall orders(2) showing compatibility with various backbones, and (3) offering robust extendibility to integrate diverse manual rules tailored to specific business needs.

In modern recommendation systems, leveraging multiple types of user-item interaction behaviors (e.g., click, add-to-cart, and purchase) presents both advantages and challenges. Recent studies organize multi-behavior data into heterogeneous bipartite graphs and used graph neural networks to learn latent representations. However, these methods struggle to model higher-order interactions and capture complex dependencies across various behaviors. In this paper, we propose a novel graph construction method that converts multi-behavior interactions into dual star-expansion hypergraphs by introducing a new type of node called hypernode. Subsequently, we develop StarRec, a hypergraph-based framework for multi-behavior recommendation. StarRec utilizes a spatial-based two-stage intra-behavior message passing strategy and a cross-behavior propagation layer to accurately and efficiently propagate information, modeling both high-order and cross-behavior relationships through the hypergraphs. This approach yields comprehensive representations that enhance recommendation performance. Experimental results on two real-world datasets demonstrate the superiority of StarRec. Our extensive experiments show that StarRec significantly outperforms state-of-the-art methods while maintaining competitive model scalability.

To address the business needs of industrial recommendation systems, an increasing number of Multi-Domain Recommendation (MDR) methods are designed to improve recommendation performance on multiple domains simultaneously. Most MDR methods follow a multi-task learning paradigm, suffering from poor deployability and negative transfer. Due to the great success of large pre-trained models, the pre-train & fine-tune paradigm is attracting increasing attention. The latest methods introduce parameter-efficient fine-tuning techniques like prompt-tuning, showcasing high efficiency and effectiveness. However, these methods neglect the fundamental differences between recommendation and NLP tasks. The inadequate capacity of recommendation models restricts the effectiveness of prompts and adapters. Worse still, traditional natural domain division may group non-identically distributed samples into the same domain, violating the assumption of independent and identically distributed (i.i.d.) data. In this paper, we propose MultiLoRA, a Multi-directional Low Rank Adaptation paradigm for multi-domain recommendation. First we pre-train a universal model using all data samples. Then we conduct multiple domain divisions on the sample space. Under each division, we fine-tune the pre-trained model to obtain a set of domain-specific LoRAs. Finally, we learn a LoRA fusion module to integrate domain-specific preference patterns across multiple divisions. Experimental results on real-world datasets demonstrate notable advantages of MultiLoRA: (1) achieving SOTA performance, (2) showcasing remarkable compatibility, and (3) proving highly efficient, featuring only 2% trainable parameters compared to the backbone.

Cross-Domain Recommendation (CDR) is a promising technique to alleviate data sparsity by transferring knowledge across domains. However, the negative transfer issue in the presence of numerous domains has received limited attention. Most existing methods transfer all information from source domains to the target domain without distinction. This introduces harmful noise and irrelevant features, resulting in suboptimal performance. Although some methods decompose user features into domain-specific and domain-shared components, they fail to consider other causes of negative transfer. Worse still, we argue that simple feature decomposition is insufficient for multi-domain scenarios. To bridge this gap, we propose TrineCDR, the TRIple-level kNowledge transferability Enhanced model for multi-target CDR. Unlike previous methods, TrineCDR captures single domain and targeted cross-domain embeddings to serve multi-domain recommendation. For the latter, we identify three fundamental causes of negative transfer, ranging from micro to macro perspectives, and correspondingly enhance knowledge transferability at three different levels: the feature level, the interaction level, and the domain level. Through these efforts, TrineCDR effectively filters out noise and irrelevant information from source domains, leading to more comprehensive and accurate representations in the target domain. We extensively evaluate the proposed model on real-world datasets, sampled from Amazon and Douban, under both dual-target and multi-target scenarios. The experimental results demonstrate the superiority of TrineCDR over state-of-the-art cross-domain recommendation methods.