The task personalized recommendation problems in software crowdsourcing systems have unique characteristics, i.e., large task flow, high task complexity, long development cycle, winning task competitions, professional ability requirements, etc. However, existing software crowdsourcing recommendation mechanisms do not consider the contextual information of crowdsourcing tasks. In particular, the effects of crowdsourcing workers' interest changes and capability constraints on task selection are usually ignored. Therefore, this study proposes a new worker capability-correction long- and short-term attention network (CLSAN) recommendation framework. Firstly, explicit and implicit features are obtained from the feature data of crowdsourcing workers and historical crowdsourcing tasks. The long-term and short-term feature layers are extracted by adding LSTM to the historical tasks; the attention weight of user preference is calculated by integrating the attention mechanism to obtain each user's personalized preference. Secondly, this study considers that the capabilities of the worker need to be matched with the skills required for the software task. We design a worker capability correction model that uses Word2Vec software to obtain competency similarities between crowdsourcing workers and tasks, thereby determining the priority of tasks that satisfy the worker's capability. The experimental results show that CLSAN can accurately evaluate the changes of interest preferences of crowdsourcing workers, and effectively improve the quality and efficiency of crowdsourcing recommendations.

Professional technical documents (PTDs) offer a wealth of information for R&D personnel and innovation management scholars. Recently, the increase in the categories and volume of PTDs has introduced new challenges for their automatic and accurate classification. Existing studies have focused on leveraging the semantic information of documents (i.e., titles and abstracts) for classification tasks. However, the standard label hierarchy of classification systems and the rich label semantic information have been generally ignored. In this paper, we propose a supervised learning-based classification model, designed to Make Full Use of Label Information (MFULI) for hierarchical multi-label PTD classification. Firstly, we deploy a Label-aware Supervised Contrastive Learning Module (LSCLM), which introduces the definition of label set similarity with the aim of improving document representation. Then, we propose a Hierarchy-aware Label Embedding Attentive Module (HLEAM) that dynamically incorporates label hierarchy information into the classification model. We evaluate our proposed model on two public patent datasets, namely USPTO-1 and WIPO-alpha. Experimental results show that our model outperforms other state-of-the-art classification models. Furthermore, we perform a series of ablation studies and analyses to demonstrate the necessity of each component of our model. This paper provides important theoretical contributions and practical implications for innovation and technology management.

1. In early 2024, China’s new energy vehicle (NEV) market surpassed a 50% retail penetration rate (J. Li et al., 2024), with young consumers (aged 24–30, constituting 43% of NEV followers (Sykora e...

This study addresses the need to transform enterprise scientific and technological intelligence (STI) services from a discrete resource-supply model toward a more systematic value-creation approach, an important challenge in the digital transformation of knowledge-intensive industries. As an exploratory qualitative inquiry, this work combines large language model-assisted analysis with grounded theory to examine the construction logic and operational mechanisms of an embedded intelligent STI service system. Drawing on in-depth interviews with STI professionals, a qualitative corpus was analyzed using human–machine collaborative coding to systematically derive and organize key constructs. The findings yield a preliminary three-layer conceptual framework: “supply-demand interactive matching, organizational embedded services, and digital-intelligent platform support.” Specifically, the supply–demand matching layer facilitates targeted alignment through demand insight, dynamic response, and quality closed-loop management; the organizational embedded service layer delivers intelligence through scenario integration, process integration, and responsibility–authority integration; and the digital-intelligent platform support layer enables core capabilities via data element induction, intelligent diffusion, and tacit knowledge conversion. The proposed framework offers an initial, structured perspective on how embedded intelligent STI services may operate, providing a foundational reference for both research and practice in this emerging domain.