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[Purpose/Significance] This paper constructs a dynamic knowledge recommendation service model oriented to online academic communities based on ternary interactive determinism to provide a theoretical basis for online community multi-dimensional demand analysis and demand evolution trend description, to improve the knowledge community recommendation services. [Method/Process] Firstly, based on the ternary interactive determinism, we clarify the internal and external factors that affect the user's knowledge needs and analyze the relationship between the factors. Secondly, according to the needs analysis objective of the ternary interactive determinism, we clarify the corresponding analysis methods and extract the characteristics of users' knowledge needs in various dimensions. Finally, we integrate the demand characteristics of various dimensions and build a knowledge demand chain to describe the evolution of user demand under the interaction of the three elements. We use the similarity of the demand chain to calculate and predict the future knowledge demand of users to expand the analysis of users' knowledge demand. [Results/Conclusions] The knowledge recommendation service system based on the ternary interactive determinism fully considers the various influencing factors of user needs from a global perspective, improves the fine-grained characterization of user needs in the community, and provides reference for academic communities to improve their knowledge service levels.

To proactively assist engineers in finding and reusing massive design lesson-learned knowledge (DLK), knowledge recommendation has become a key technology of knowledge management. However, in collaborative product design, complex multitask context information disrupts the perception of engineers’ knowledge needs for every single task. In this situation, traditional knowledge recommendation approach is prone to provide a mixed DLK recommendation list, thus resulting in a lack of pertinence and low accuracy. Facing these challenges, scarcely any reports on context-aware knowledge recommendation in the multitask environment of collaborative product design. Aiming to fill this gap, a multitask context-aware DLK recommendation approach is proposed to assist collaborative product design in a smarter manner. The mutual interference of context information from different tasks is addressed by preprocessing works, multitask knowledge need awareness, DLK recommendation engine, respectively. Therefore, the proposed approach not only effectively acquires engineers’ knowledge needs from different task contexts and pertinently provides the corresponding DLK recommendation list for each task but also guarantees the accuracy of DLK recommendation in multitask context of collaborative product design. To validate the proposed approach, a DLK recommendation system is implemented in a shipbuilding scenario, and some comparative experiments are carried out. Experimental results show that the proposed approach outperforms conventional approaches in the aspects of effectiveness and performance. Therefore, it opens up a promising way to help engineers reuse needed DLK in collaborative product design.

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

With the advancement of artificial intelligence and educational informatization, there is a growing demand for intelligent teaching assistance systems in universities. Focusing on the university “Algorithms” course in the computer science department, this study develops a multi-terminal collaborative knowledge service system, Course-Oriented Knowledge Service–Based AI Teaching Assistant System (CKS-AITAS), which consists of a PC terminal and a mobile terminal, where the PC terminal integrates functions including knowledge graph, semantic retrieval, intelligent question-answering, and knowledge recommendation. While the mobile terminal enables classroom check-in and teaching interaction, thus forming a closed-loop platform for teaching organization, resource acquisition, and knowledge inquiry. For the document retrieval module, paragraph-level semantic modeling of textbook content is conducted using Word2Vec, combined with approximate nearest neighbor indexing, and this module achieves an MRR@10 of 0.641 and an average query time of 0.128 s, balancing accuracy and efficiency; the intelligent question-answering module, based on a self-built course FAQ dataset, is trained via the BERT model to enable question matching and answer retrieval, achieving an accuracy rate of 86.3% and an average response time of 0.31 s. Overall, CKS-AITAS meets the core teaching needs of the course, provides an AI-empowered solution for university teaching, and boasts promising application prospects in facilitating education sustainability.

New product development is knowledge intensive as it needs the work teams and design engineers located at various locations to constantly share, update, and re-use knowledge. As such, improving the efficiency of acquiring knowledge and coping with the challenge of frequently retrieving related knowledge have become a key factor to managing knowledge in new product development. This paper combines rough set theory and information entropy to establish a new knowledge recommender technique to address the issue of knowledge reuse for new product development. Our method enhances knowledge acquisition and reuse, as it provides a realistic framework for knowledge acquisition and reuse, encompassing the entire process from what the design and work teams need, to recommending what they should have. To validate the proposed approach, we perform experiments on a case study to demonstrate the benefit and performance.

Knowledge recommendation plays a crucial role in online learning platforms. It aims to optimize the service quality so as to improve users’ learning efficiency and outcomes. Existing approaches generally leverage RNN-based methods in combination with attention mechanisms to learn user preference. There is a lack of in-depth understanding of users’ knowledge-level changes over time and the impact of knowledge item categories on recommendation performance. To this end, we propose the knowledge-level-evolution and category-aware personalized knowledge recommendation (KLECA) model. The model firstly leverages bidirectional GRU and the time adjustment function to understand users’ learning evolution by analyzing their learning trajectory data. Secondly, it considers the effect of item categories and descriptive information and enhances the accuracy of knowledge recommendation by introducing a cross-head decorrelation module to capture the information of knowledge items based on a multi-head attention mechanism. In addition, a personalized attention mechanism and gated function are introduced to grab the relationship between items, item categories and user learning trajectory to strengthen the representation of information. Through extensive experiments on real-world data collected from an online learning platform, the proposed approach has been shown to significantly outperform other approaches.

When automated pattern matching tools are used to execute user-knowledge pattern matching (UKPM) in the expert knowledge recommendation system (EKRS), user-knowledge matching is uncertain and the matching efficiency is low. To solve the above problems, the dynamic UKPM mathematical model is established and the “Entropy-Beta” method of crowdsourcing task assignment is designed to solve the model in the study. Firstly, the concept of Entropy is combined with crowdsourcing. The uncertainty of user-knowledge matching results is measured and the magnitude of the uncertainty is calculated. Secondly, based on the Beta distribution function, the accuracy of matching results is measured. The optimal matching results are selected and the matching results were sent to EKRS according to the matching probability. Thirdly, the knowledge recommendation process of UKPM is dynamically adjusted according to the matching probability. Finally, the comparison results of several algorithms showed that the Entropy-Beta algorithm could largely improve the accuracy, efficiency, dynamic regulation, and other performances of EKRS.

During the cloud-based product form design process, traditional collaborative-filtering recommendation methods fail to effectively calculate similarity metrics or generate relevant knowledge recommendations for newly joined designers, due to their lack of historical knowledge scores, resulting in inefficient knowledge acquisition. Since designers show a clear tendency of professional trust in the knowledge adoption process, they are more inclined to accept knowledge resources recommended by people with similar professional backgrounds to theirs or by authorities in their fields. Therefore, this paper proposes a knowledge recommendation method for product form design integrating crowd-intelligence context similarity and trust relationships in cloud environments. The method first constructs an ontology model and a product form design knowledge ontology, containing task context, designer’s context, and computational context to facilitate the acquisition, storage, processing, and invocation of contextual information and knowledge. Second, the neighboring set of target designers is determined by calculating the multidimensional contextual similarity and trust relationship between designers. Finally, the missing knowledge score of the target designer is predicted by the knowledge evaluation of the neighboring designers, and the recommendation list is generated. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design.

In the era of information and communication technology (ICT), the advancement of science and technology has led to a trend of diversification in map representation. However, the lack of professional knowledge means that there is still a challenge in determining the appropriate type of thematic map for land use expression. To address this issue, this paper proposes a knowledge recommendation method for land use thematic maps based on the theory of visualization dimensions. Firstly, we establish a knowledge ontology of land use thematic maps centered on spatial data, data characteristics, visualization dimensions, thematic map forms, and application scenarios. A land use thematic map knowledge graph is constructed through knowledge extraction and storage operations. Secondly, knowledge embedding is performed on the knowledge graph to enable the knowledge-based expression of map visualization elements. Finally, based on the knowledge elements of land use thematic expression, a similarity calculation model is established to calculate the similarity between input data and the spatial data characteristics, visualization dimensions, and application scenarios within the knowledge graph, deriving a comprehensive similarity result to achieve precise recommendation for land use thematic map forms. The results show that the method can provide a more accurate visualization reference for the selection of land use themes, meeting the diversified needs of land use thematic expression to a certain extent.