Sequential recommendation aims to predict the next item a user is likely to interact with based on their historical interaction sequence. Capturing user intent is crucial in this process, as each interaction is typically driven by specific intentions (e.g., buying skincare products for skin maintenance, buying makeup for cosmetic purposes, etc.). However, users often have multiple, dynamically changing intents, making it challenging for models to accurately learn these intents when relying on the entire historical sequence as input. To address this, we propose a novel framework called Intent Oriented Contrastive Learning for Sequential Recommendation (IOCLRec). This framework begins by segmenting users’ sequential behaviors into multiple subsequences, which represent the coarse-grained intents of users at different points in their interaction history. These subsequences form the basis for the three contrastive learning modules within IOCLRec. The fine-grained intent contrastive learning module uncovers detailed intent representations, while the single-intent and multi-intent contrastive learning modules utilize intent-oriented data augmentation operators to capture the diverse intents of users. These three modules work synergistically, driving comprehensive performance optimization in intricate sequential recommendation scenarios. Our method has been extensively evaluated on four public datasets, demonstrating superior effectiveness.

Dense retrieval has now become the mainstream paradigm in information retrieval. The core idea of dense retrieval is to align document embeddings with their corresponding query embeddings by maximizing their dot product. The current training data is quite sparse, with each document typically associated with only one or a few labeled queries. However, a single document can be retrieved by multiple different queries. Aligning a document with just one or a limited number of labeled queries results in a loss of its semantic information. In this paper, we propose a training-free Potential Query Retrieval (PQR) framework to address this issue. Specifically, we use a Gaussian mixture distribution to model all potential queries for a document, aiming to capture its comprehensive semantic information. To obtain this distribution, we introduce three sampling strategies to sample a large number of potential queries for each document and encode them into a semantic space. Using these sampled queries, we employ the Expectation-Maximization algorithm to estimate parameters of the distribution. Finally, we also propose a method to calculate similarity scores between user queries and documents under the PQR framework. Extensive experiments demonstrate the effectiveness of the proposed method.

Electrocardiogram (ECG) diagnosis aims to automatically classify ECG recordings into clinically meaningful categories, playing a vital role in medical decision-making. Deep learning methods, while promising, demand extensive annotated data and lack interpretability. Large Language Models (LLMs) offer potential in low-data scenarios and generating interpretable outputs, yet their application to ECG diagnosis, especially leveraging multimodal data (e.g., raw signals, derived features, and clinical knowledge), remains underexplored. To address these challenges, we propose ECG-Doctor, an interpretable and multimodal ECG diagnosis framework based on LLMs. ECG-Doctor comprises four key components: (1) ECG Knowledge Acquisition Module, which integrates external medical knowledge and Chain-of-Thought (CoT) reasoning to address the inability of LLMs to follow standardized ECG diagnostic procedures; (2) ECG Feature Extraction Module, which incorporates domain knowledge to overcome LLMs' limitations in comprehensively understanding structured ECG features; (3) ECG Waveform Analysis Module, which introduces time-series ECG models to equip LLMs with the capability to interpret and reason over raw ECG signal morphologies; (4) KNN-based ECG Retrieval Module, which retrieves the top-k most similar ECG samples and guides LLMs through in-context learning (ICL), enabling them to differentiate and learn from variations across ECGs. The outputs of these modules are aggregated and provided to the LLM as diagnostic context, enabling ICL to perform comprehensive ECG diagnosis. This design effectively simulates the diagnostic reasoning process of experienced electrocardiologists. Extensive experiments on the PTB-XL dataset demonstrate that ECG-Doctor is compatible with various LLMs and consistently outperforms existing baselines at both 100 Hz and 500 Hz sampling rates, showcasing its strong versatility and robustness. Furthermore, ECG-Doctor provides well-grounded diagnostic explanations, highlighting its superior interpretability.

Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user preferences. In the current setup of SRS, a common configuration is to uniformly consider each historical behavior as a positive interaction. However, this setting has the potential to yield sub-optimal performance as each individual item often have a different impact on shaping the user's interests. Hence, in this paper, we propose a novel automatic sampling framework for sequential recommendation, named AutoSAM, to non-uniformly treat historical behaviors. Specifically, AutoSAM extends the conventional SRS framework by integrating an extra sampler to intelligently discern the skew distribution of the raw input, and then sample informative sub-sets to build more generalizable SRS. To tackle the challenges posed by non-differentiable sampling actions and to introduce multiple decision factors for sampling, we further design a novel reinforcement learning based method to guide the training of the sampler. Furthermore, we theoretically devise multi-objective sampling rewards including Future Prediction and Sequence Perplexity, and then optimize the whole framework in an end-to-end manner by combining the policy gradient. We conduct extensive experiments on benchmark recommendation models and four real-world datasets. The experimental results demonstrate the effectiveness of the proposed AutoSAM.

Modern recommender systems face a critical challenge in modeling the intricate interplay between multi-behavior interactions of users (e.g., clicks, adds-to-cart and purchases) and temporal dynamics that drive evolving preferences. While existing multi-behavior sequential recommendation methods attempt to capture these signals, they often suffer from fragmented modeling, such as decoupling behaviors and items into separate sequences, neglecting time-aware transitions, or relying on computationally intensive architectures that hinder real-world scalability. To address these limitations, we propose GEneralized Alternating Regressor (GEAR), a novel framework that unifies behaviors, items, and temporal contexts into a single autoregressive sequence through an alternating architecture. At its core, GEAR represents user interactions as triplets and processes them through a modular transformer architecture. In this architecture, each triplet is alternately modeled at lower layers to disentangle fine-grained patterns, while upper layers jointly learn cross-signal dependencies. This design mimics the interlocking mechanism of gears, enabling the seamless transitions between multi-behavior dynamics and item transitions. Additionally, we incorporate a time-bias term to quantify the decay of behavioral influence across both short- and long-term horizons. Extensive experiments on real-world datasets validate the effectiveness, generalizability, and computational efficiency of the proposed framework.

Chinese Spelling Check (CSC) aims to identify and correct spelling errors in Chinese texts, where enhanced semantic understanding of a sentence can significantly improve correction accuracy.Recently, Large Language Models (LLMs) have demonstrated exceptional mastery of world knowledge and semantic understanding, rendering them more robust against spelling errors.However, the application of LLMs in CSC is a double-edged sword, as they tend to unnecessarily alter sentence length and modify rare but correctly used phrases.In this paper, by leveraging the capabilities of LLMs while mitigating their limitations, we propose a novel plug-and-play Alignment-and-Replacement Module (ARM) that enhances the performance of existing CSC models and without the need for retraining or fine-tuning.Experiment results and analysis on three benchmark datasets demonstrate the effectiveness and competitiveness of the proposed module.