Effective customer support requires not only accurate problem-solving but also structured and empathetic communication aligned with professional standards. However, existing dialogue datasets often lack strategic guidance, and real-world service data is difficult to access and annotate. To address this, we introduce the task of Customer Support Conversation (CSC), aimed at training customer service supporters to respond using well-defined support strategies. We propose a structured CSC framework grounded in COPC guidelines, defining five conversational stages and twelve strategies to guide high-quality interactions. Based on this, we construct CSConv, an evaluation dataset of 1,855 real-world customer–agent conversations rewritten using LLMs to reflect deliberate strategy use, and annotated accordingly. Additionally, we develop a role-playing approach that simulates strategy-rich conversations using LLM-powered roles aligned with the CSC framework, resulting in the training dataset RoleCS. Experiments show that fine-tuning strong LLMs on RoleCS significantly improves their ability to generate high-quality, strategy-aligned responses on CSConv. Human evaluations further confirm gains in problem resolution.

The emergence of Large Vision-Language Models (LVLMs) has substantially expanded model capabilities beyond text-only understanding, enabling unified inference across both visual and textual modalities and supporting a broader range of real-world applications.To comprehensively evaluate the perception, understanding, reasoning, and cognition capabilities of LVLMs throughout the entire financial business workflow in Chinese contexts, we introduce CFMME, a novel Chinese financial multimodal evaluation benchmark.CFMME comprises 6,052 instances spanning from fundamental academic knowledge to complex realworld applications, covering eight primary financial image modalities and four core multimodal tasks.On CFMME, we conduct a thorough evaluation of representative LVLMs.The results show that the state-of-the-art model attains an overall accuracy of 66.11% on the question answering task and an average score of 77.18 on the detection, recognition, and information extraction tasks, indicating substantial room for improvement in current LVLMs.In addition, we conduct detailed analyses of error causes, cross-modal capabilities, and multiorientation settings, yielding valuable insights for future research.We hope that CFMME will spur further progress in LVLMs, especially by improving their performance on multiple multimodal tasks in the financial domain.

Video popularity prediction plays a foundational role in many aspects of life, such as recommendation systems and investment consulting. Because of its technological and economic importance, this problem has been extensively studied for years. However, four constraints have limited most related works' usability. First, most feature oriented models are inadequate in the social media environment, because many videos are published with no specific content features, such as a strong cast or a famous script. Second, many studies assume that there is a linear correlation existing between view counts from early and later days, but this is not the case in every scenario. Third, numerous works just take view counts into consideration, but discount associated sentiments. Nevertheless, it is the public opinions that directly drive a video's final success/failure. Also, many related approaches rely on a network topology, but such topologies are unavailable in many situations. Here, we propose a Dual Sentimental Hawkes Process (DSHP) to cope with all the problems above. DSHP's innovations are reflected in three ways: (1) it breaks the "Linear Correlation" assumption, and implements Hawkes Process; (2) it reveals deeper factors that affect a video's popularity; and (3) it is topology free. We evaluate DSHP on four types of videos: Movies, TV Episodes, Music Videos, and Online News, and compare its performance against 6 widely used models, including Translation Model, Multiple Linear Regression, KNN Regression, ARMA, Reinforced Poisson Process, and Univariate Hawkes Process. Our model outperforms all of the others, which indicates a promising application prospect.

Product specifications are often available for a product on E-commerce websites. However, novice customers often do not have enough knowledge to understand all features of a product, especially advanced features. In order to provide useful knowledge to the customers, we propose to automatically generate augmented product specifications, which contains relevant opinions for product feature values, feature importance, and product-specific words. Specifically, we propose a novel Specification Latent Dirichlet Allocation (SpecLDA) that can enable us to effectively model product reviews and specifications at the same time. It mines review texts relevant to a feature value in order to inform customers what other customers have said about the feature value in reviews of the same product and also different products. SpecLDA can also infer importance of each feature and infer which words are special for each product so that customers quickly understand products. Experiment results show that SpecLDA can effectively model product reviews with specifications. The model can be used for any text collections with specification (key-value) type prior knowledge.

With the rapid development of Internet and E-commerce, abundant product reviews have been written by consumers who bought the products. These reviews are very useful for consumers to optimize their purchasing decisions. However, since the reviews are all written by consumers who have bought and used a product, there are generally very few or even no reviews available for a new product or an unpopular product. We study the novel problem of retrieving relevant opinion sentences from the reviews of other products using specifications of a new or unpopular product as query. Our key idea is to leverage product specifications to assess product similarity between the query product and other products and extract relevant opinion sentences from the similar products where a consumer may find useful discussions. Then, we provide ranked opinion sentences for the query product that has no user-generated reviews. We first propose a popular summarization method and its modified version to solve the problem. Then, we propose our novel probabilistic methods. Experiment results show that the proposed methods can effectively retrieve useful opinion sentences for products that have no reviews.

Sentiment analysis studies the public opinions towards an entity, and it is an important research area in data mining. Recently, a lot of sentiment analysis models have been proposed, including supervised and unsupervised approaches. However, the role of supervised models has been undermined by the phenomenon of big data, and the unsupervised ones are drawing more and more attention. But, most current unsupervised methods are based on Latent Dirichlet Allocation (LDA), and they need to specify the number of aspects in advance, making them subjective. In addition, these methods treat factual words and opinioned words the same, and assume that one sentence contains only one aspect, all of which make the existing unsupervised methods unsatisfactory. To solve these problems, this paper proposes a novel hybrid Hierarchical Dirichlet Process-Latent Dirichlet Allocation (HDP-LDA) model. This model can automatically determine the number of aspects, distinguish factual words from opinioned words, and further effectively extracts the aspect specific sentiment words. Experiment result shows that our model can clearly capture the aspects people mentioned and the specific sentiment words they use in each aspect, improving the performance of sentiment analysis efficiently. At last, we compared our model with the influential topic models, namely, JST, AUSM and Maxine-LDA, on the online restaurant review, and found our model performs very well.

We present NegativeRank, a novel graph-based sentence ranking model to improve the diversity of focused summary by performing random walks over sentence graph with negative edge weights. Unlike the typical eigenvector centrality ranking, our method models the redundancy among sentence nodes as the negative edges. The negative edges can be thought of as the propagation of disapproval votes which can be used to penalize redundant sentences. As the iterative process continues, the initial ranking score of a given node will be adjusted according to a long-term negative endorsement from other sentence nodes. The evaluation results confirm that our proposed method is very effective in improving the diversity of the focused summary, compared to several well-known text summarization methods.

With the rapid outstripping of healthcare resources by the demands on hospital care, it is important to find more effective and efficient ways for managing care. This research is aimed at developing new admission prediction models using various pre-hospital variables to help hospital estimate the patients to be admitted. We developed a framework of hospital admission prediction and proposed two novel approaches to capture semantics of chief complaints to enhance prediction. Our experiments on a hospital dataset demonstrated that our proposed models outperformed several benchmark methods.