Zhaoheng Huang, Dacheng Wen, Yutao Zhu, Xiaoying Lian, Yushi Liang, Kai Hao, Nan Li, Liangjie Zhang, Qi Zhang, Ji-Rong Wen, Zhicheng Dou, Fangzhao Wu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Multi-image understanding is crucial in real-world applications such as social media analysis and news reporting. However, existing benchmarks fall short in evaluating models' ability to integrate external knowledge and perform cross-image reasoning. To address this gap, we introduce MRBench, a comprehensive benchmark designed to assess knowledge-based reasoning across 12 diverse domains, incorporating four types of image relations: visually similar, identical entities, attribute-associated, and independent images. Additionally, we propose Multimodal Adaptive Retrieval Reasoning (MARR), a novel framework that enables the analysis of relationships among multiple input images and adaptively determines when to terminate the retrieval process. Extensive evaluations of state-of-the-art multimodal large language models (MLLMs) show a notable gap between model and human performance. The best-performing model, Gemini 2.0, reaches 56.86% accuracy, still 20.24% below humans. Proprietary models generally surpass open-source ones, particularly on visually similar and same-entity tasks, underscoring current limits in multi-image reasoning and retrieval and positioning MRBench as a key diagnostic tool. Our benchmark is available for further research and development in this field. https://github.com/Bruce-XJChen/MRBench.

Sponsored search advertisements (ads) appear next to search results when consumers look for products and services on search engines. As the fundamental basis of search ads, relevance modeling has attracted increasing attention due to the significant research challenges and tremendous practical value. In this paper, we address the problem of multi-modal modeling in sponsored search, which models the relevance between user query and commercial ads with multi-modal structured information. To solve this problem, we propose a transformer architecture with Ads data on Commercial Visual-Linguistic Representation (AdsCVLR) with contrastive learning that naturally extends the transformer encoder with the complementary multi-modal inputs, serving as a strong aggregator of image-text features. We also make a public advertising dataset, which includes 480K labeled query-ad pairwise data with structured information of image, title, seller, description, and so on. Empirically, we evaluate the AdsCVLR model over the large industry dataset, and the experimental results of online/offline tests show the superiority of our method.

Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present highly non-Euclidean anatomy with various types of geometric patterns (i.e., tree-likeness and cyclic structures). The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries. Empirically, our proposal is extensively evaluated over five real-world datasets, and the experimental results demonstrate the superiority of GDCF.

Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep learning based document representation and ANN search techniques are allied to handle this task. However, a major challenge is that the ANN index can be too large to fit into memory, given the considerable size of answer corpus. In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification. For the best of retrieval accuracy, a Progressive Optimization framework is designed. The sparse embeddings are learned ahead for high-quality search of candidates. Conditioned on the candidate distribution induced by the sparse embeddings, the dense embeddings are continuously learned to optimize the discrimination of ground-truth from the shortlisted candidates. Besides, two techniques: the contrastive quantization and the locality-centric sampling are introduced for the learning of sparse and dense embeddings, which substantially contribute to their performances. Thanks to the above features, our method effectively handles massive-scale EBR with strong advantages in accuracy: with up to recall gain on million-scale corpus, and up to recall gain on billion-scale corpus. Besides, Our method is applied to a major sponsored search platform with substantial gains on revenue (), Recall () and CTR (). Our code is available at https://github.com/microsoft/BiDR.

Sponsored search ads appear next to search results when people look for products and services on search engines. In recent years, they have become one of the most lucrative channels for marketing. As the fundamental basis of search ads, relevance modeling has attracted increasing attention due to the significant research challenges and tremendous practical value. Most existing approaches solely rely on the semantic information in the input query-ad pair, while the pure semantic information in the short ads data is not sufficient to fully identify user's search intents. Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling. In this paper, we extensively investigate how to naturally fuse the semantic textual information with the user behavior graph, and further propose three novel AdsGNN models to aggregate topological neighborhood from the perspectives of nodes, edges and tokens. Furthermore, two critical but rarely investigated problems, domain-specific pre-training and long-tail ads matching, are studied thoroughly. Empirically, we evaluate the AdsGNN models over the large industry dataset, and the experimental results of online/offline tests consistently demonstrate the superiority of our proposal.

Click-through rate (CTR) prediction aims to recall the advertisements that users are interested in and to lead users to click, which is of critical importance for a variety of online advertising systems. In practice, CTR prediction is generally formulated as a conventional binary classification problem, where the clicked advertisements are positive samples and the others are negative samples. However, directly treating unclicked advertisements as negative samples would suffer from the severe label noise issue, since there exist many reasons why users are interested in a few advertisements but do not click. To address such serious issue, we propose a reinforcement learning based noise filtering approach, dubbed RLNF, which employs a noise filter to select effective negative samples. In RLNF, such selected, effective negative samples can be used to enhance the CTR prediction model, and meanwhile the effectiveness of the noise filter can be enhanced through reinforcement learning using the performance of CTR prediction model as reward. Actually, by alternating the enhancements of the noise filter and the CTR prediction model, the performance of both the noise filter and the CTR prediction model is improved. In our experiments, we equip 7 state-of-the-art CTR prediction models with RLNF. Extensive experiments on a public dataset and an industrial dataset present that RLNF significantly improves the performance of all these 7 CTR prediction models, which indicates both the effectiveness and the generality of RLNF.

Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks. Low latency variants of these models have also been developed in recent years in order to apply them in the field of sponsored search which has strict computational constraints. However these models are not the panacea to solve all the Natural Language Understanding (NLU) challenges as the pure semantic information in the data is not sufficient to fully identify the user intents. We propose the TextGNN model that naturally extends the strong twin tower structured encoders with the complementary graph information from user historical behaviors, which serves as a natural guide to help us better understand the intents and hence generate better language representations. The model inherits all the benefits of twin tower models such as C-DSSM and TwinBERT so that it can still be used in the low latency environment while achieving a significant performance gain than the strong encoder-only counterpart baseline models in both offline evaluations and online production system. In offline experiments, the model achieves a 0.14% overall increase in ROC-AUC with a 1% increased accuracy for long-tail low-frequency Ads, and in the online A/B testing, the model shows a 2.03% increase in Revenue Per Mille with a 2.32% decrease in Ad defect rate.

We propose a novel training scheme for fast matching models in Search Ads, motivated by practical challenges. The first challenge stems from the pursuit of high throughput, which prohibits the deployment of inseparable architectures, and hence greatly limits model accuracy. The second problem arises from the heavy dependency on human provided labels, which are expensive and time-consuming to collect, yet how to leverage unlabeled search log data is rarely studied. The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data. Specifically, we first construct multiple auxiliary tasks from the enumerated relevance labels, and train the annotators by jointly learning from those related tasks. The annotation models are then used to assign scores to both labeled and unlabeled training samples. The deployable model is firstly learnt on the scored unlabeled data, and then fine-tuned on scored labeled data, by leveraging both labels and scores via minimizing the proposed label-aware weighted loss. According to our experiments, compared with the baseline that directly learns from relevance labels, training by the proposed framework outperforms it by a large margin, and improves data efficiency substantially by dispensing with 80% labeled samples. The proposed framework allows us to improve the fast matching model by learning from stronger annotators while keeping its architecture unchanged. Meanwhile, it offers a principled manner to leverage search log data in the training phase, which could effectively alleviate our dependency on human provided labels.

With the rapid development of social networks services (SNS), Enterprise micro logging gradually shows its importance in business management. In the Enterprise Micro logging, If we can Accurately search and rank the experts, it can greatly enhance the operational rate of the Enterprise. In this article, Based on Google's PageRank algorithm, combined with the labels of the expert on enterprise microblogging network, we can rank the experts in the enterprise microblogging network according to the influence of the expert. Therefore, we can improve the precision rate of the expert searching results for the user.

With the gradually subsided of economic crisis, China's fast moving consumer goods industry has present slow growth trend. But its market share is still not high. According to the characteristics of China's fast moving consumer goods industry, it is necessary to carry out the strategic transformation at this stage. This article departure from the beer industry, a particular fast moving consumer goods industey, combined the importance of the forecast sales orders in the supply chain, to discuss the new strategic transformation model of the beer industry.

This paper introduces the research status of the business model at home and abroad, including the concept of business model, business model classification, the relationship between the business model classification and enterprise performance, and puts forward the shortcomings of study on the business model classification. Based on previous studies, this paper puts forward a new business model classification according to Chinese IT industry characters. Then enterprise performance is divided into four aspects: profitability, growth, operating efficiency and market value. This paper introduces a single-factor analysis of variance, and the process of studying the effects of different business models on the various aspects of enterprise performance in order to find a better business model, and the method can provide recommendations for the enterprise business model transformation.

Different queries require different ranking methods. It is however challenging to determine what queries are similar, and how to rank documents for them. In this paper, we propose a new method to cluster queries according to the similarity determined based on URLs in their answers. We then train specific ranking models for each query cluster. In addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. The proposed approach is tested using data from a search engine. It turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries.