Personalized news recommendation aims to recommend candidate news to the target user. Since the data and knowledge involved in traditional recommender systems are restricted, recent studies utilize large language models (LLMs) to generate news articles and augment the original dataset. However, despite the superiority of LLM-based augmentation in news recommendation, previous studies still suffer from two serious problems, i.e., structure-level deficiency and semantic-level noise. Since the LLM-based augmentation is mainly implemented at the semantic level, collaborative signals, the critical structure information in recommender systems, is neglected during the generation process. Thus, it is inappropriate to perform recommendation based on the augmented user-news bipartite, which manifests as multiple isolated cliques. Moreover, utilizing the open-world knowledge of LLMs to extend the closed systems will inevitably introduce noise information, leading to difficulties in mining users' real preferences. In this paper, we propose a novel Structure-aware and Semantic-aware approach for LLM-Empowered personalized News Recommendation, named S^2LENR, to tackle the mentioned problems. Specifically, we propose a structure-aware refinement module to inject collaborative information in a parametric way, in order to construct a valid augmented bipartite. Besides, we devise a semantic-aware denoising module utilizing contrastive learning paradigm to overcome the negative effects of noise information. Finally, we calculate the relevance score between target user and candidate news representations. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.

In the dynamic landscape of large-scale web search, Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query, which is essential for improving user engagement and facilitating rapid decision-making. Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications. However, these approaches suffer from two key limitations: 1) The multi-stage pipeline often introduces cumulative information loss and architectural bottlenecks due to its weakest component; 2) Traditional models lack sufficient semantic understanding of both user queries and documents, particularly when dealing with complex search intents. In this study, we propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search. Our approach integrates large model distillation, supervised fine-tuning, direct preference optimization, and lookahead decoding to transform a lightweight model with only 0.1B parameters into a domain-specialized QDTS expert. Evaluated on multiple industry-relevant metrics, our model outperforms the production baseline and achieves a new state of the art. Furthermore, it demonstrates excellent deployment efficiency, requiring only 334 NVIDIA L20 GPUs to handle ~50,000 queries per second under 55~ms average latency per query.

Multimodal Large Language Models (MLLMs) excel in various tasks, yet often struggle with modality bias, where the model tends to rely heavily on a single modality and overlook critical information in other modalities, which leads to incorrect focus and generating irrelevant responses. In this paper, we propose using the paradigm of preference optimization to solve the modality bias problem, including RLAIF-V-Bias, a debiased preference optimization dataset, and a Noise-Aware Preference Optimization (NaPO) algorithm. Specifically, we first construct the dataset by introducing perturbations to reduce the informational content of certain modalities, compelling the model to rely on a specific modality when generating negative responses. To address the inevitable noise in automatically constructed data, we combine the noise-robust Mean Absolute Error (MAE) with the Binary Cross-Entropy (BCE) in Direct Preference Optimization (DPO) by a negative Box-Cox transformation, and dynamically adjust the algorithm’s noise robustness based on the evaluated noise levels in the data. Extensive experiments validate our approach, demonstrating not only its effectiveness in mitigating modality bias but also its significant role in minimizing hallucinations. The code and data is available at https://github.com/zhangzef/NaPO.

Cross-domain recommendation (CDR) has been proven as a promising way to alleviate the cold-start issue, in which the most critical problem is how to draw an informative user representation in the target domain via the transfer of user preference existing in the source domain. Prior efforts mostly follow the embedding-and-mapping paradigm, which first integrate the preference into user representation in the source domain, and then perform a mapping function on this representation to the target domain. However, they focus on mapping features across domains, neglecting to explicitly model the preference integration process, which may lead to learning coarse user representation. Diffusion models (DMs), which contribute to more accurate user/item representations due to their explicit information injection capability, have achieved promising performance in recommendation systems. Nevertheless, these DMs-based methods cannot directly account for valuable user preference in other domains, leading to challenges in adapting to the transfer of preference for cold-start users. Consequently, the feasibility of DMs for CDR remains underexplored. To this end, we explore to utilize the explicit information injection capability of DMs for user preference integration and propose a Preference-Guided Diffusion Model for CDR to cold-start users, termed as DMCDR. Specifically, we leverage a preference encoder to establish the preference guidance signal with the user's interaction history in the source domain. Then, we explicitly inject the preference guidance signal into the user representation step by step to guide the reverse process, and ultimately generate the personalized user representation in the target domain, thus achieving the transfer of user preference across domains. Furthermore, we comprehensively explore the impact of six DMs-based variants on CDR. Extensive experiments on three real-world CDR scenarios demonstrate the superiority of our DMCDR over SOTA methods and six DMs-based variants.

Video Quality Assessment (VQA) is a crucial component of broadscale video retrieval systems. Its goal is to accurately identify various quality issues in videos, thereby encouraging the video retrieval system to prioritize high-quality videos. In large-scale industrial video retrieval systems, we formulate the characteristics of low-quality videos into four categories: visual-related low-level quality problems such as mosaics and black boxes, textual-related low-level quality problems caused by video title and Optical Character Recognition (OCR) content, as well as semantic-level frame incoherence and frame-text mismatch caused by emerging AI-generated videos. These kinds of low-quality videos, which are widely present in industrial environments, have been overlooked in academic research before, and accurately identifying them is very challenging. In this paper, we introduce a Multi-Branch Collaborative learning Network (MBCN) to tackle the above issues. We carefully design four assessment branches for MBCN to adapt to the above four kinds of issues for industrial video retrieval systems. After obtaining independent scores for each branch, we perform a weighted aggregation of the various branches to dynamically address video quality issues in different scenarios with a squeeze-and-excitation mechanism. Finally, we integrate point-wise and pair-wise optimization objectives to ensure the predicted scores are stable and fall into a reasonable range. To demonstrate the effectiveness of our proposed MBCN, we conduct extensive offline and online experiments in a world-level video search engine. The experimental results show that due to the powerful ability of MBCN to identify video quality issues, the ranking ability of the video retrieval system has been significantly improved. We also conduct a series of detailed experimental analyses to verify that all four evaluation branches play a positive role. Besides that, for emerging low-quality AI-generated videos, the recognition accuracy of MBCN also improves significantly compared to the baseline.

Text-video retrieval is a challenging task that aims to identify relevant videos given textual queries. Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content. Previous works primarily focus on aligning the query and the video by finely aggregating word-frame matching signals. Inspired by the human cognitive process of modularly judging the relevance between text and video, the judgment needs high-order matching signal due to the consecutive and complex nature of video contents. In this paper, we propose chunk-level text-video matching, where the query chunks are extracted to describe a specific retrieval unit, and the video chunks are segmented into distinct clips from videos. We formulate the chunk-level matching as n-ary correlations modeling between words of the query and frames of the video and introduce a multi-modal hypergraph for n-ary correlation modeling. By representing textual units and video frames as nodes and using hyperedges to depict their relationships, a multi-modal hypergraph is constructed. In this way, the query and the video can be aligned in a high-order semantic space. In addition, to enhance the model's generalization ability, the extracted features are fed into a variational inference component for computation, obtaining the variational representation under the Gaussian distribution. The incorporation of hypergraphs and variational inference allows our model to capture complex, n-ary interactions among textual and visual contents. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the text-video retrieval task.

Reading text to extract relational facts has been a long-standing goal in natural language processing. It becomes especially challenging when the extraction scope is extended to document level, where multiple entities in a document generally exhibit complex intra- and inter-sentence relations. In this paper, we propose a novel Multi-granularity Heterogeneous Graph (MHG) to tackle this challenge. Specifically, we define four types of nodes with different granularities and eight types of edges based on heuristic rules, entrusting the MHG two major advantages. On the one hand, it connects any two entities with a short path in the graph to better handle the complex inter-sentence interactions between entities. On the other hand, it enables rich interactions among nodes with different granularities to promote accurate multi-hop reasoning. Experimental results on the largest document-level relation extraction dataset suggest that the proposed model achieves new state-of-the-art performance.

Document-level relation extraction (RE)poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.In this paper, we propose a novel graphbased model with Dual-tier Heterogeneous Graph (DHG) for document-level RE.In particular, DHG is composed of a structure modeling layer followed by a relation reasoning layer.The major advantage is that it is capable of not only capturing both the sequential and structural information of documents but also mixing them together to benefit for multi-hop reasoning and final decisionmaking.Furthermore, we employ Graph Neural Networks (GNNs) based message propagation strategy to accumulate information on DHG.Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two widely used datasets, and further analyses suggest that all the modules in our model are indispensable for document-level RE.

Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.There are lots of redundant phrases between extracted sentences, but it is difficult to model them exactly by the general supervised methods.Previous sentence encoders, especially BERT, specialize in modeling the relationship between source sentences.While, they have no ability to consider the overlaps of the target selected summary, and there are inherent dependencies among target labels of sentences.In this paper, we propose HAHSum (as shorthand for Hierarchical Attentive Heterogeneous Graph for Text Summarization), which well models different levels of information, including words and sentences, and spotlights redundancy dependencies between sentences.Our approach iteratively refines the sentence representations with redundancy-aware graph and delivers the label dependencies by message passing.Experiments on large scale benchmark corpus (CNN/DM, NYT, and NEWSROOM) demonstrate that HAHSum yields ground-breaking performance and outperforms previous extractive summarizers.