AI Knows What Workers Need: Knowledge Graphs Power Proactive Union Services

Every large organization faces the same quiet problem: the services designed to help employees often reach the wrong people, at the wrong time, in the wrong form. Trade unions inside power grid enterprises sit on mountains of information—complaint records, safety training logs, activity sign-ups, policy documents—yet the systems that deliver rights-and-interests services to workers typically run on manual processing or rigid, static rules. A new study published in Complex & Intelligent Systems argues that this mismatch can be fixed with a familiar recipe from consumer technology: recommendation algorithms, but rebuilt for the workplace and grounded in structured organizational knowledge.

The research, led by Xuyu Chen and colleagues at the Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., introduces an active recommendation algorithm that combines two technologies: a domain knowledge graph and dynamic user personas. The goal is not simply to push more content at employees, but to anticipate which rights-and-interests services—benefits guidance, dispute resolution, safety programs, training opportunities—a given worker is likely to need, and to deliver those services before the employee has to ask. In their evaluation on real business data, the system achieved a Precision@10 of 83.5 percent and a Recall@10 of 81.6 percent, with an average response time of just 135 milliseconds.

To understand why this matters, it helps to consider what the existing approach gets wrong. Static rule matching treats an employee as a fixed profile: a job title, a department, a seniority level. But needs shift constantly. A field technician who recently logged a safety complaint has different immediate priorities from the same technician six months earlier, before an incident or a policy change. Manual processing, meanwhile, cannot scale across thousands of workers and dozens of service categories. The authors note that these limitations leave employees’ changing needs across job roles, policy contexts, and service scenarios largely uncaptured—a gap the new framework is designed to close.

The first pillar of the system is the knowledge graph. Rather than treating policy documents and service records as isolated text, the researchers construct a graph in which entities—employees, services, regulations, complaints, training programs—are nodes, and the relationships between them are explicit, machine-readable edges. Building this graph requires enhanced entity recognition, the natural language processing task of identifying meaningful terms in unstructured text, followed by relation extraction, which determines how those entities connect. Once assembled, the graph is compressed into knowledge graph embeddings: dense numerical vectors that encode the structure of the network so that a machine learning model can reason over it efficiently.

The embeddings are what make the graph computationally useful. In a raw graph, asking whether a particular grievance-handling service is relevant to a particular line worker requires traversing links and interpreting labels. In the embedded space, every entity becomes a point in a high-dimensional coordinate system where related entities sit close together. Similarity becomes arithmetic. A recommendation engine can measure the distance between an employee’s situation and a service offering, or trace which paths through the graph justify a match. That last property—traceability—is central to the authors’ emphasis on interpretability, because a recommendation that can point to the specific policy, complaint record, or role relationship that triggered it is far easier for union administrators to trust and audit than an opaque score.

The second pillar is dynamic user persona modeling. Where traditional user profiles are snapshots, personas in this framework are living summaries that capture recent changes in employee behavior. If a worker begins attending safety trainings, files a benefits inquiry, or interacts with complaint channels, those signals update the persona, shifting the vector that represents the person’s current needs. This recency weighting matters in an organizational setting, where a single event—a workplace incident, a policy revision, a seasonal workload change—can abruptly redefine what services are relevant. The persona acts as a bridge between the messy stream of behavioral data and the structured knowledge graph.

The recommendation engine itself is a hybrid. It fuses three signal sources: collaborative filtering, the classic technique that infers preferences from the behavior of similar users; the knowledge graph embeddings, which inject domain structure and policy context; and the persona features, which encode each employee’s evolving situation. Hybrid architectures of this kind are standard in modern recommender systems precisely because no single signal is sufficient. Collaborative filtering struggles with sparse data and cannot explain itself; knowledge graphs are rich in context but depend on the completeness of the graph; personas capture dynamics but can overfit to short-term noise. Combining them lets each component compensate for the weaknesses of the others, producing recommendations that are simultaneously personalized, contextually grounded, and explainable.

The evaluation used real business data from a power grid enterprise, spanning employee records, service transactions, complaints, safety events, training participation, and activity sign-ups. Before any modeling, the researchers standardized the data and applied privacy protections, fuzzifying sensitive fields and de-identifying personal information—a necessary step when the underlying records concern labor rights and grievances. Under a unified evaluation protocol, the hybrid model reached an F1-Score of 82.5 percent, balancing precision and recall, while maintaining a response time of 135 milliseconds, fast enough for interactive use. Those numbers suggest the system can operate not as a batch reporting tool but as a live service layer that responds to employees in real time.

The broader significance extends beyond one utility company. Organizational service recommendation is an underexplored cousin of the consumer systems that power streaming and shopping platforms, but it carries different stakes and different constraints. The data is more sensitive, the notion of relevance is tied to wellbeing and legal rights rather than engagement, and the users—union administrators and employees alike—need to understand why a recommendation appeared. By anchoring the system in an explicit knowledge graph and keeping the recommendation logic interpretable, the authors offer a template for how institutions can automate proactive support without sacrificing accountability. The open-access publication, released under a Creative Commons license, makes the full technical apparatus, including complete pseudocode and a summary of all formula symbols, available for other teams to examine and adapt.

There are, of course, caveats. The results come from a single enterprise’s data, and performance in other organizational contexts—with different service catalogs, record-keeping practices, and graph densities—remains to be demonstrated. The paper is also an early-access version, subject to further editorial revision before the final Version of Record. Still, the core finding stands: integrating structured domain knowledge with dynamic user personas measurably improves recommendation accuracy, coverage, and interpretability in organizational service settings. For the employees of one Chinese power grid, that could mean the right support arriving before it is requested. For the field of applied AI, it is another sign that recommendation technology is migrating from the marketplace into the workplace, where the currency being matched is not entertainment or products, but rights and care.

Subject of Research: A knowledge graph and dynamic user persona-based recommendation algorithm for proactive trade union services in power grid enterprises

Article Title: An active recommendation algorithm for trade union rights and interests services in power grid enterprises based on knowledge graphs and dynamic user personas

Article References: Chen, X., Song, C., Lin, H., & Li, K. (2026). An active recommendation algorithm for trade union rights and interests services in power grid enterprises based on knowledge graphs and dynamic user personas. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-026-02486-y

Image Credits: AI Generated

DOI: 10.1007/s40747-026-02486-y

Keywords: knowledge graph, recommendation algorithm, dynamic user personas, trade unions, power grid enterprises, personalized services, collaborative filtering, knowledge graph embedding, machine learning, employee services, interpretability, Complex & Intelligent Systems