Jinrui Wang, Zhenfeng Gao, Wendan Wang, Huili Wang, Zichen Qin, Linjie Zhu, Hongke Fu, Shangguang Wang, Tao Qi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.

Existing Web service eco-systems are typically managed in a centralized manner, which hinders their further development due to inherent disadvantages such as trust issues, interest disputes, value separation and so on. The recently emerged blockchains provide distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states, which provide a natural solution. Based on the INKchain, which is an open-source permissioned blockchain mechanism extending the Hyperledger Fabric, this paper proposes Trusted Decentralized Service Eco-System (T-DSES). T-DSES achieves not only fundamental functionalities of conventional systems, but also offers mechanisms to stimulate participants to bring trustworthiness to the whole system. The trustworthiness of T-DSES is realized by three strategies: reliable information of services and mashups, reliable records of participants’ rights, and reliable measurement of participants’ contributions. A customized token “SToken” is created to act as the media of value circulation. In this paper, the overall framework and detailed design of T-DSES are presented, especially including how to utilize Kubernetes to establish a cloud-based environment. A tailored Web front-end ensures the usability of operations. Over real-world data from ProgrammableWeb.com, analyses and experiments have been conducted to verify the feasibility and effectiveness of the presented approach.

Service ecosystems, consisting of various kinds of services and mashups, usually keep evolving over time.Existing works on the evolution of service ecosystems focus on either evaluating the impacts of single services' changes on the usage of services and the stability of the whole ecosystem, or discovering co-occurrence relationship between services, but fail to disclose any knowledge from the aspect of the evolution of service composition patterns.Based on our previous work, this paper moves one step further, revealing the latent service composition trends in a service ecosystem and providing more distinct explanation of different topic evolution patterns.A novel methodology, named Extended Dependency-Compensated Service Co-occurrence LDA (EDC-SeCo-LDA), is developed to calculate the directed dependencies between different topics and build topic evolution graph.The evolution trend

Service composition remains an important topic where recommendation is widely recognized as a core mechanism. Existing works on service recommendation typically examine either association rules from mashup-service usage records, or latent topics from service descriptions. This paper moves one step further, by studying latent topic models over service collaboration history. A concept of service co-occurrence topic is coined, equipped with a mechanism developed to construct service co-occurrence documents. The key idea is to treat each service as a document and its co-occurring services as the bag of words in that document. Four gauges are constructed to measure self-co-occurrence of a specific service. A theoretical approach, Service Co-occurrence LDA (SeCo-LDA), is developed to extract latent service co-occurrence topics, including representative services and words, temporal strength, and services' impact on topics. Such derived knowledge of topics will help to reveal the trend of service composition, understand collaboration behaviors among services and lead to better service recommendation. To verify the effectiveness and efficiency of our approach, experiments on a real-world data set were conducted. Compared with methods of Apriori, content matching based on service description, and LDA using mashup-service usage records, our experiments show that SeCo-LDA can recommend service composition more effectively, i.e., 5% better in terms of Mean Average Precision than baselines.

Existing service ecosystems typically rely on some centralized service registries (e.g., ProgrammableWeb.com) as "middle people" to record service behaviors thus to provide service ranking and recommendation. Excessive centralization increasingly becomes the bottleneck and hinders the further growth of the service ecosystems. As the first attempt to apply the fundamental technique underneath the emerging Bitcoin network into the field of service oriented computing, this paper proposes to build a service ecosystem as a decentralized blockchain-oriented service network, called Decentralized Service Eco-System (DSES). Whenever any activity occurs in the system (e.g., APIs are used together in a published mashup), all involved parties will individually store and maintain a copy of the detailed record (provenance) at their local databases. Such a distributed database-oriented solution will enable services who do not fully trust each other to maintain a set of global states. In this way, service discovery and recommendation can be realized in a distributed manner that promises higher scalability and maintainability. As a proof of concept, a prototyping system of DSES is constructed using the real-world data from ProgrammableWeb.com, based on the INKchain, a newly open-source consortium blockchain mechanism extending the Hyperledger Fabric.

With the booming number of web services, it is a challenge for inexperienced developers to select suitable services and make service compositions. Therefore, recommending services based on user queries becomes a necessity. For modeling the queries and services' descriptions, many recent studies are based on LDA (Latent Dirichlet Allocation). However, some previous empirical works indicate that LDA model doesn't gain high accuracy in generating latent presentation which is subject to the restrictive assumption of the Dirichlet-Multinomial distribution. In this paper, we propose a Time-aware Collaborative Poisson Factorization (TCPF) to tackle the problem. TCPF takes Poisson Factorization as the foundation to model mashup queries and service descriptions separately, and incorporate them with the historical usage data together using collective matrix factorization. Experiments on the real-world ProgrammableWeb dataset show that our model outperforms the state-of-the-art methods (e.g., Time-aware collaborative domain regression) by 7.7% in terms of mean average precision, and costs much less time on the sparse, massive and long-tailed data set.

Service ecosystem consists of all kinds of services, and some of them may be composed by developers to create new mashups. Existing work on service recommendation and composition mine either frequent patterns from mashup-service usage records, or latent topics from service metadata. In this paper, we propose Service Co-occurrence LDA (SeCo-LDA), a novel approach that mines latent topic models over service co-occurrence patterns. The key idea is to treat each service as a document, and its bag of co-occurring services as the bag of words in that document. Using this model, we can analyze such service co-occurrence documents with a probabilistic topic model. We show how to derive service co-occurrence topics, and then validate our model on the real-world ProgrammableWeb.com dataset. We illustrate that SeCo-LDA can discover meaningful latent service composition patterns including their temporal strength and services' impacts, which conventional Apriori can not reveal. Comparing with Apriori, content matching based on service description and LDA directly using mashup-service usage records, we have demonstrated that SeCo-LDA can recommend service composition more effectively, 5% better in terms of MAP than the baseline approach.