Lujia Yang, Weicai Yan, Yongbo He, Qifei Zhang, Tao Jin, Jinshan Zhang, Meng Xi, Jianwei Yin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are rife with missing views and continuously emerging classes, which pose significant obstacles to robust learning. Prevailing methods are ill-equipped for this reality, as they either lack adaptability to new classes or incur exponential parameter growth when handling all possible missing-view patterns, severely limiting their scalability in web environments. To systematically address this gap, we formally introduce a novel task, termed incomplete multi-view multi-label class incremental learning (IMvMLCIL), which requires models to simultaneously address heterogeneous missing views and dynamic class expansion. To tackle this task, we propose E2PL, an Effective and Efficient Prompt Learning framework for IMvMLCIL. E2PL unifies two novel prompt designs: task-tailored prompts for class-incremental adaptation and missing-aware prompts for the flexible integration of arbitrary view-missing scenarios. To fundamentally address the exponential parameter explosion inherent in missing-aware prompts, we devise an efficient prototype tensorization module, which leverages atomic tensor decomposition to elegantly reduce the prompt parameter complexity from exponential to linear w.r.t. the number of views. We further incorporate a dynamic contrastive learning strategy explicitly model the complex dependencies among diverse missing-view patterns, thus enhancing the model's robustness. Extensive experiments on three benchmarks demonstrate that E2PL consistently outperforms state-of-the-art methods in both effectiveness and efficiency. The codes and datasets are available at https://anonymous.4open.science/r/code-for-E2PL.

Data cleaning (DC) is a crucial yet challenging step for many data engineering tasks. Traditional pre-configuration DC methods rely heavily on predefined rules or constraints, demanding significant domain knowledge and manual effort. While configuration-free DC approaches have been explored, they still demand extensive feature engineering or labeled data for intensive model training. In this paper, we propose azero-training and interpretable DCsystem, named${\sf ZeroDC}$, that leverageslarge language models(LLMs) to generate data cleaning rules and chain-of-thoughts (CoTs), without the need for model training.${\sf ZeroDC}$consists of two modules,iterative detection rule generation(IDG) andtraining-free explainable correction(TEC). To generate high-quality error detection rules with minimal human feedback, IDG first bootstraps a set of rules viacontrastive rule initiationon sampled syntactic and semantic contrastive pairs. It then progressively enhances them through aniterative rule refinementworkflow that selects the most informative elements for updates. TEC constructs acontextual-relevant tuple retrieverusing aweighted cosine similarityfunction to efficiently identify the most relevant tuples for each dirty value, reducing redundancy in the LLM prompts and lowering computational costs. It further prompts for generatingcorrection CoTsfor user-corrected representative values, as well as prompts for creatingcorrection rulesandexplainable corrections, which automatically provide explanations for correction results, all without the need for model training. Extensive experiments conducted on various real-world datasets demonstrate that${\sf ZeroDC}$achieves, on average, a 5.36% increase in accuracy and an 8.16x speedup compared to state-of-the-art methods. The codes and datasets of this paper are available athttps://github.com/YangChen32768/ZeroDC.

A service ecosystem refers to a multilateral network composed of heterogeneous service entities, where the exchange of data, resources, and value through interactions among specific participants forms a service pattern. As service ecosystems like virtual hospital alliance (VHA) evolve towards large-scale, multi-domain integration to meet complex user needs, service pattern fusion has emerged as a fundamental approach to leverage data, resources, and value aggregation. By converging elements from multiple patterns, service pattern fusion enables the fulfillment of composite business objectives with reduced redundancy and lower costs. Existing works primarily address fusion requirements by reorganizing existing services through approaches such as service composition and business process management where only service functions and workflows are considered. However, they lack formalization of pattern fusion constraints and fail to support comprehensive integration of participants, data, resources, and value, let alone identifying optimal fusion solutions that account for participant collaboration and service integration. In this study, we formally define the Service Pattern Fusion Problem (SPFP) as an optimization task aimed at identifying the most efficient and cost-effective pattern by integrating, combining, and pruning elements from multiple patterns while preserving their objectives and meeting business constraints. We adapt traditional heuristic methods to SPFP and propose the Fusion-Oriented Confidence-Aware genetic algorithm (FoCa). FoCa dynamically adjusts the search space and transition probabilities in each iteration, achieving optimal fusion results with a 41.09% reduction in pattern loss and the fastest convergence. In addition, we designed a set of pattern features and conducted random fusion experiments on the public service pattern dataset S-SPD, to explore the correlation between those features and the optimization magnitude across various metrics. The analysis helps identify which types of service patterns benefit most from fusion, providing valuable insights for researchers and practitioners in both academic and engineering contexts.

Portfolio optimization is one of the most important financial problem, suffering from huge computational pressure due to arithmetic complexity. Quantum computing offers polynomial or even exponential speedup that turns out to be a promising approach. However, existing quantum methods is fundamentally limited by either poor scalability or insufficient accuracy. In this paper, we propose SAPO, which formally articulates the quantum circuit that seamlessly integrates financial theory and historical data characteristics with quantum algebra. The circuit design is extended from the HHL algorithm incorporating mean-variance theory, which promotes scalability by equivalent transformation. Then, we present a min-max eigenvalue model that leverages historical financial information to refine parameter settings with high accuracy. Experiments conducted on market data demonstrate that SAPO can effectively reduce the complexity by $\mathbf{3 6. 9 4 \%}$ compared to basic HHL [1], [2] and improve the accuracy by $1.46 \times$ compared to hybrid HHL [3].

Correcting missing or erroneous data values is an essential task in data cleaning. Traditional pre-configuration error correction (EC) methods rely heavily on predefined rules or constraints, demanding significant domain knowledge and manual effort. While configuration-free EC approaches have been explored, they still demand extensive feature engineering or labeled data for intensive model training. In this paper, we propose a zero-training and interpretable EC system, named ZeroEC, that leverages large language models (LLMs) to generate chain-of-thoughts (CoTs) and correction rules for EC, without the need for model training. ZeroEC consists of two modules, contextual-relevant tuple search (CTS) and training-free explainable correction (TEC). CTS constructs a contextual-relevant tuple retriever using a weighted cosine similarity function to efficiently identify the most relevant tuples for each dirty tuple, reducing redundancy in the LLM prompts and lowering computational costs. TEC employs a clustering-based representative tuple sampling strategy to alleviate “hallucination” risk by exposing LLMs to diverse types of data errors. It further prompts for generating correction CoTs for user-corrected representative tuples, as well as prompts for creating correction rules and explainable ECs, which automatically provide explanations for EC, all without the need for model training. Extensive experiments conducted on various real-world datasets demonstrate that ZeroEC achieves a 66.82% increase in accuracy and a 6.87x speedup compared to state-of-the-art methods. The codes and datasets of this paper are available at https://github.com/YangChen32768/ZeroEC.

Recently, multimodal recommendation (MMRec) has received much attention, which models user preferences based on both user behaviors and modality information. Although current graph neural network based methods yield notable results in MMRec, certain limitations persist among these methods. 1) Most methods rely on pre-trained networks to extract modality features but fail to remove modality noise. 2) Recent methods leverage InfoNCE strategy to align representation, while ignoring the effect of feature redundancy and lacking sufficient alignment between different modality features. Such limitations ultimately harm the recommendation performance. To this end, we propose a Dual Mutual Information-Driven Multimodal Recommendation Model with Denoising Graph Autoencoder (DMIGA). Specifically, to reduce the noise within modality features, we design a denoising graph autoencoder with a cross-modal consistency constraint. Furthermore, we propose a dual mutual information learning mechanism on both feature and instance levels, to reduce the feature redundancy and align different representations. Experimental results on three real-world datasets consistently demonstrate that DMIGA outperforms state-of-the-art methods, with an average of 3.8% improvement.

With the vast range of available services, it has become an important challenge to recommend the optimal service for mashup developer. Recent studies are mainly limited by the service similarity, resulting in challenges such as discrepancy in textual semantics, implicity of inter-service relationships, and the sparsity of historical interactions. Service bundles, which offer a set of services, present a novel approach to address the mashup development problem. In this work, we propose an innovative message-passing model, a Heterogeneous Graph AutoEncoderbased service bundle recommendation model (HGAE), to tackle the issues. Specifically, we introduce the Graph Propagation Module to encode potentially implicit semantic relations in the Mashup-Service-Bundle heterogeneous graph. Furthermore, we build a unified representation for the bundle in the Bundle Prediction Module by combining an autoencoder and spatial attention mechanism, enabling the integration of relationships across different node and edge types. Extensive experiments on real-world datasets demonstrate that HGAE notably outperforms state-of-the-art methods on all metrics, with improvements of 8.69% in NDCG and 9.55% in Recall on the ProgrammableWeb dataset.

Driven by the flexibility, reliability, and scalability of microservice architecture, an increasing number of enterprises are decomposing monolithic applications into microservices. However, existing deep learning-based decomposition methods rely heavily on partition number selection, which, if unreasonable, can lead to frequent microservice communication and reduced performance. Moreover, manual partition suggestions not only decrease automation but also fail to adapt to rapid business iteration, and existing methods inadequately capture the relationship characteristics between application classes. To address these issues, this paper proposes a novel graph-based partitioning technique, GGRME. It constructs a system dependency graph through static, dynamic, and semantic analysis, and then employs a self-supervised gated graph neural network combined with cross-supervised optimization for community detection to automatically generate microservice decomposition results. Experiments demonstrate that GGRME outperforms benchmark methods in 65 % of tests, yielding superior microservice decomposition performance.

Missing values in multivariate time series data present significant challenges to effective analysis. Existing methods for multivariate time series analysis either ignore missing data, sacrificing performance, or follow the impute-then-analyze paradigm, which suffers from redundant training and error accumulation, leading to biased results and suboptimal performance. In this paper, we propose INTER, a novel end-to-end framework for incomplete multivariate time series analysis, which bypasses imputation by leveraging pre-trained language models to learn the distribution of incomplete time series data. INTER incorporates two novel components: the missing-rate-aware time series patch-dropping (MPD) strategy and the missing-aware Transformer block, both of which we propose to enhance model generalization, robustness, and the ability to capture underlying patterns in the observed incomplete time series. Moreover, we theoretically prove that the MPD strategy exhibits lower sample variance for time series with the same dropout rate compared to other dropping strategies. Extensive experiments on 11 public real-world time series datasets demonstrate that INTER improves accuracy by over 20% compared to state-of-the-art methods, while maintaining competitive computational efficiency.

Incomplete multi-view partial multi-label classification (IMvPMLC), which tackles the combined challenges of incompleteness in both multi-view and multi-label problems, has drawn considerable attention. Existing IMvPMLC methods have made progress but still face several challenges: (i) They mainly focus on the consistency of representations across multiple views but overlook the relationships among instances, leading to suboptimal representations. (ii) They primarily utilize only the available labels for supervised learning, ignoring the missing label distribution and limiting their ability to capture label correlations. In this paper, we propose a novel model named Dual Structure-guided Contrastive Network (DSCN) for IMvPMLC. Specifically, we introduce a similarity-guided instance-level contrastive learning mechanism to achieve multi-view consistent and discriminative representations across instances by leveraging instance structures, while a multi-view attention-based fusion strategy dynamically facilitates the fusion of multi-view representations to derive a robust consensus representation. Then, we design a multi-view shared classifier integrated with a correlation-guided label-level contrastive learning mechanism to enhance predictions by leveraging complementary information across multiple views and capturing label structures, effectively exploiting missing label distribution. Extensive experiments on five benchmark datasets demonstrate that, DSCN yields a more than 13% accuracy, compared with the state-of-the-art approaches. The code and datasets are available at https://anonymous.4open.science/r/DSCN-D471.

Portrait animation aims to transfer the facial expressions and movements of a target character onto a reference character. This task presents two main challenges: accurately transferring motion and expressions while fully preserving the identity features of the reference portrait. We introduce Vividportraits, a diffusion-based model designed to effectively meet these objectives. In contrast to existing methods that rely on sparse representations such as facial landmarks, our approach leverages facial parsing maps for motion guidance, enabling a more precise conveyance of subtle expressions. A random scaling technique is applied during training to prevent the model from internalizing identity-specific features from the driving images. Furthermore, we perform foreground-background segmentation on the reference portrait to reduce data redundancy. The long-video generation process is refined to improve consistency across sequences. Our model, exclusively trained on public datasets, demonstrates superior performance relative to current state-of-the-art methods, achieving a notable 8% improvement in expression metric. More visual results are available on the anonymous website https://www.vividportraits.cn.

In the context of the development of the modern service industry, emerging technologies such as the Internet of Things (IoT) and 5G have promoted the integration of a large number of service devices, increasing the complexity of the service ecosystem and accelerating its evolution process. Although the service model has summarized the business relationship in the service ecosystem from the four aspects of workflow, data flow, resource flow and value flow, it has not formed a systematic description of the heterogeneous devices where the service is deployed. Therefore, future service ecosystem modeling methods need to solve the following two problems: how to describe heterogeneous devices to provide guidance for the deployment of services, and how to enable the service ecosystem to adapt to the dynamic adjustment of services.In this paper, in order to solve the problem of dynamic addition and deletion of services and dynamic replacement of deployment carriers, we propose a service deployment description method(SDDM) based on holon concept, and integrate it with service pattern, then extend service pattern description language , namely carrier SPDL (SPDL-C). To validate our framework, we empirically conducted a case study in which we selected an intelligent warehouse management service pattern as the object of study to reveal how our approach could address future challenges. Finally, we summarize and discuss the innovation and significance of the work.

Recent years have witnessed remarkable advances in graph representation learning using Graph Neural Networks (GNNs). To fully exploit the unlabeled graphs, researchers pre-train GNNs on large-scale graph databases and then fine-tune these pre-trained G raph M odels (GMs) for better performance in downstream tasks. Because different GMs are developed with diverse pre-training tasks or datasets, they can be complementary to each other for a more complete knowledge base. Naturally, a compelling question is emerging: How can we exploit the diverse knowledge captured by different GMs simultaneously in downstream tasks? In this paper, we make one of the first attempts to exploit multiple GMs to advance the performance in the downstream tasks. More specifically, for homogeneous GMs that share the same model architecture but are obtained with different pre-training tasks or datasets, we align each layer of these GMs and then aggregate them adaptively on a per-sample basis with a tailored Recurrent Aggregation Policy Network (RAPNet). For heterogeneous GMs with different model architectures, we design an alignment module to align the output of diverse GMs and a meta-learner to decide the importance of each GM conditioned on each sample automatically before aggregating the GMs. Extensive experiments in various downstream tasks from 3 domains reveal our dominance over each single GM. Additionally, our methods (UniGM) can achieve better performance with moderate computational overhead compared to alternative approaches including ensemble and model fusion. Also, we verify that our methods are not limited to graph data but could be flexibly applied to multiple modalities. The codes are available at https://github.com/monica309673/UniGM.

Recommendation is a key mechanism for modern users to access items of their interests from massive entities and information. Recently, graph contrastive learning (GCL) has demonstrated satisfactory results on recommendation, due to its ability to enhance representation by integrating graph neural networks (GNNs) with contrastive learning. However, those methods often generate contrastive views by performing random perturbation on edges or embeddings, which is likely to bring noise in representation learning. Besides, in all these methods, the degree of user preference on items is omitted during the representation learning process, which may cause incomplete user/item modeling. To address these limitations, we propose the Adaptive Fusion of Multi-View Graph Contrastive Recommendation (AMGCR) model. Specifically, to generate the informative and less noisy views for better contrastive learning, we design four view generators to learn the edge weights focusing on weight adjustment, feature transformation, neighbor aggregation, and attention mechanism, respectively. Then, we employ an adaptive multi-view fusion module to combine different views from both the view-shared and the view-specific levels. Moreover, to make the model capable of capturing preference information during the learning process, we further adopt a preference refinement strategy on the fused contrastive view. Experimental results on three real-world datasets demonstrate that AMGCR consistently outperforms the state-of-the-art methods, with average improvements of over 10% in terms of Recall and NDCG. Our code is available on https://github.com/Du-danger/AMGCR.

Innovation and prosperity of the Modern Service Industry bring convenience and efficiency to our society. However, the service governance capability lags behind the development of the industry, causing problems such as service violations and poor service quality. Existing works towards service regulation for service design time such as business process compliance checking are restricted to certain types of rules. When services or policies undergo evolution, rapid iteration becomes challenging. Motivated by this, we propose a service regulation analysis framework for service design time. It includes three phases: the service regulation modeling phase, which realizes modeling of regulation requirements; the service violation recognition phase, proposing an automatic detection algorithm based on process semantics; and the violation trace-back phase, which supports rapid localization of violations. Based on previous work, we construct the Enhanced-LPD4VR, a dataset with a broader range of processes and more nuanced annotations. Furthermore, we introduce an Internet healthcare service case study that illustrates the effectiveness of our framework through comparative experiments.

With the growth of cloud computing, a large number of innovative mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed for Mashup creation, but most either treat different feature factor interactions equally or solely rely on requirements for API recommendation. These approaches face several challenges such as API compatibility dependencies, ambiguous definition and boundary dilemmas of APIs, and sparse API invocation records. In this work, we propose a Semantic-Enhanced Heterogeneous Graph Network(SEHGN) for Mashup creation. To address the above deficiencies, we design a multi-semantic aggregator to capture semantic associations between features to encode multiple node-edge relationships. Then, we introduce a semantic embedding component to generate text embedding vectors for mashups and APIs to learn global and local semantic information about text documents at different levels of abstraction. Finally, we fuse the output vectors to obtain a list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that SEHGN outperforms state-of-the-art models in terms of overall and long-tail Web API recommendations.

With the increasing development of cloud computing, a large number of innovative Mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed in mashup creation, but most either assign equal weight to model factorization interactions or solely rely on requirements information for API recommendation. Unfortunately, these methods face several challenges, such as explicit and implicit dependencies among APIs, ambiguous API semantics, and the undervaluation of tail APIs. In this work, we propose a Functional and Structural Fusion Model (FSFM) based on Web API recommendation for Mashup creation. To address the above deficiencies, we first design the structural interaction component to encode the latent structural relationships in the heterogeneous network of Mashups and APIs and capture the topological structure signals between different Mashups and APIs. Then, we introduce the functional semantic component to generate text embedding vectors for Mashups and APIs, enhancing their requirement semantics at multiple levels of abstraction. Finally, we fuse the output vectors to obtain the list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that FSFM outperforms other state-of-the-art models in both overall and long-tail Web API recommendations.

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to one target domain. Although multi-source domains contain more complementary information than single source domain, MSDA involves some disturbed source samples, which will degrade the adaptation performance. To solve this problem, we propose a novel MSDA method for semantic segmentation. Specifically, to fully explore the optimal source samples for target domain, we propose a novel correlation measurement mechanism, weighing domain-level source-target correlation (DSC) and pixel-level source-target correlation (PSC). For each pair of source and target domains, DSC and PSC estimate the source-target correlations via the distances between target class prototypes and source class prototypes, and between target class prototypes and every pixel of source features, respectively. Built upon PSC, we propose a novel mix-up strategy, which pastes high-correlation source pixels to target images, to construct augmented mixing images for adaptation. Then we train the segmentor on the mixed images with pseudo labels and labeled source images, with DSC and PSC to suppress the negative effects of the low-correlation source domains and pixels. Furthermore, an attentive prototype alignment loss, based on DSC, is proposed to align target and multi-source domains, which attaches more importance to high-correlation source domains. The experimental results on the representative benchmark datasets (i.e., GTA5 and SYNTHIA → Cityscapes) highlight that our method substantially outperforms the state-of-the-art single-source domain adaptation and MSDA methods.

The service pattern is an abstraction of the business relationship among various participants from the service ecosystem in four aspects: workflow, data flow, resource flow, and value flow. In order to optimize service patterns, it is necessary to consider the collaboration between participants as well as the interaction among different servers. The existing works either optimize the former by adjusting service orchestration, such as business process optimization and workflow optimization, or focus on the latter through adjusting service distribution, such as cloud service distribution optimization and edge service deployment optimization. However, the prevalence of service ecosystems and distributed computing has begun to make multi-user, multi-server scenarios commonplace, placing greater importance on fast and effective optimization of service patterns. In this work, we summarize the constraints and objectives and formally define the service pattern optimization problem. Beyond that, we propose a service pattern optimization-oriented confidence aware recurrent simulated annealing algorithm (PooCa). Experiments conducted on an existing dataset show that our method outperforms the other three baselines on the overall dataset as well as on the eight subsets. Also, our method can reduce the number of search iterations by 41.15% on average with the same search space. We also carry out case studies on the online travel booking service pattern and investigate factors that make patterns perform better.

For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively. In this article, we propose a service pattern assessment framework which consists of two parts. As part one, we complement the service pattern description language (SPDL) with extended elements and observable attributes to empower it with quantitative analysis, namely Quantitative SPDL (SPDL-Q). In part two, a set of service pattern assessment metrics are designed to assess not only the quality of the services but also the cooperation efficiency of the participants and the orchestration effect of the service patterns elements. The proposed framework was then further validated by a case study, of which four E-commerce service patterns were studied to reveal their evolvement processes. Correlation experiments were also performed to identify the pattern features that have the greatest impact on each metric, so to provide guidance and suggestions for pattern design. Finally, the innovation and significance of the work are outlined and discussed.

For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively.

The convergence of the Internet and traditional industries gives the birth to the crossover services, which break through the boundaries of domains, enterprises, and businesses. The design of the service pattern is one of the key points to the success of crossover services. Existing works are mainly aimed at resource and process convergence, unable to guide the design of whole crossover service. To address this issue, we propose a rule-based service pattern convergence framework for crossover service, which consists of participant convergence, resource convergence and service process convergence. In addition to summarizing the general rules of convergence, we introduce semantic similarity to promote deep pattern convergence. Finally, a case study is presented to prove the operability of this framework.

As the population ages, eldercare and healthcare have become major issues in recent years. Crossover healthcare services, instead of individual ones, have become the main form of service provision. In this work, a scenario-based requirement model (SBRM) is proposed for crossover healthcare service. A DSL and a prototype system are designed based on the model as well. Our model defines the requirements as: WHO, in what SCENARIOs, what PROCESSes need to be performed, and what RULEs need to be satisfied. We verify our model in the real case of the MEH (medical, eldercare, healthcare) crossover service. SBRM supports the service better in our cases and shows satisfactory efficiency, effectiveness, and reusability.

In recent years, many new business modes and strategies are constantly emerging in e-commerce. The new retail has been one of the most successful modes. Different from the traditional business modes, it is driven by information technology (big data, Internet of Things, artificial intelligence, etc.) and centered on consumer experience. Furthermore, it reconstructs the core elements in online and offline trade to form a new business mode. Thus, the existing business modeling approaches cannot be used to analyze and describe the new retail mode. In this article, we first analyze the business characteristics and processes in new retail and redefine the core elements in e-commerce, such as people, product, and place. EMB can decouple the business aspect and technical aspect of the business systems of e-commerce. So EMB can bridge the gap between business experts and application developers. In addition, we verify EMB by the electronic certificate business of Taobao and deeply analyze its reusability, applicability, and efficiency. Finally, to demonstrate the advantages of EMB, it is compared with some other existing methods in detail.

The sentiment behind financial text has been observed to have correlations with stock market trend. Though widely discussed, the study on this topic faces the challenge coming from the lack of open dataset and labeled financial text. In this work, we collected a large amount of Chinese financial text from financial news, research report, stock BBS and corporate announcements. It contains 3 million articles about 128 stocks from 2010 to 2018. And then we proposed a model mapping from the text and latent sentiment to the abnormal market trend. It combines the posting amount, daily market index with the RBM-embedded document vector, and extracts the abnormal features via LSTM. After that a neural net is employed to identify the abnormal trend. The experimental results on our dataset show the effectiveness of our approach comparing to baseline methods.

Crossover service is a kind of services, which can provide multi-dimension service, great user experience and high values, through deeply converging services from different industries, different organizations and different value chains. Convergence is the key challenges for crossover service application. Using Alibaba's crossover service case, this paper illustrates five challenges of the service convergence process: pattern convergence, ecosystem convergence, environment convergence, quality convergence and value convergence. In addition, we propose a technical framework addressing these technical challenges, which includes all the major theories and models, techniques and methods, and tools and platforms supporting enterprises' crossover service convergence in the modeling phase, the design phase, the running phase and the management phase.

In the modern service industry, both service processes and data structures are becoming increasingly diverse and complex. In addition, interdependences exist among data, such that the use of "shoe size" data must be based on the "type of goods" data returning "shoe". This is also observed for the functions and interfaces in a system, as one can use the function "order payment" only after the function "order generation". This kind of phenomenon is rather common in service systems nowadays, especially when the service is a transboundary service such as the new retail proposed by Jack Ma. Traditional modeling methods have difficulties in handling such scenarios. There have been studies on service modeling over the past several years, and they have focused mainly on the service processes and interactions among services. In this work, we construct MeCo-TSM based on three sub-models to handle multi-entity complex service process. We verify our model in the real processes of our cooperation company and compare it with related works. MeCo-TSM supports the service better in our cases and shows satisfactory efficiency, effectiveness and reusability.

With rapid development of the Internet, all walks of life are engaged in the tide of Internet.In the era of "Internet+", traditional industries are widely developed and expand plenty of emerging business, such as online transactions, Internet finance and so on.However, various problems arise at the same time in this revolution.On one hand, business processes become increasingly intricate, and different fields may have difficulty in communication.On the other hand, developers are hard to understand the real demands from users and the rate of code reuse is not high.In order to solve these problems, we propose a middle-end and project manager (PM) oriented service language model, which could help decouple software development and user requirements, improve work efficiency and reduce development costs.

With the appearance of new retail, e-commerce has broken the traditional pattern, different categories of goods have their own unique business attributes.Taking transaction business as an example, the traditional physical goods business needs to complete the transaction through the logistics, while the new electronic voucher business achieves that by involving shop verification and the transaction will be totally completed after the consumption of the virtual goods such as the QQ coin.Traditional integral modeling that describes all businesses through a process has been hard to meet such a scenario.In recent years, there have been a lot of studies on business process modeling.These methods mainly focus on the process and data level and do not support business modeling well.In this work, we construct a Business Identification Model(BIM) based on four business sources to handle unique complex business process.We develop a platform based on BIM and verify our model in the real processes of our cooperation company.In addition, BIM supports assembling and reusing in business-level in our case.