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Titlebook: Knowledge Management in Organizations; 14th International C Lorna Uden,I-Hsien Ting,Juan Manuel Corchado Conference proceedings 2019 Spring

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31#
發(fā)表于 2025-3-26 23:57:37 | 只看該作者
Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relation
32#
發(fā)表于 2025-3-27 01:28:06 | 只看該作者
Talents Evaluation Modeling Based on Fuzzy Mathematics it transforms qualitative evaluation into quantitative evaluation, and has been widely applied in many fields. In order to make the recruitment process of HRM (Human Resource Management) more systematic and rational, this paper introduces a method to evaluate talents by applying fuzzy synthetic eva
33#
發(fā)表于 2025-3-27 08:26:14 | 只看該作者
Understanding Consumers’ Continuance Intention Toward Self-service Stores: An Integrated Model of thal services are gradually being replaced. Self-service businesses are emerging one after the other, including self-service laundries, gas stations, car washes, ticketing machines, and even self-service stores. This is not merely a new trend, but a revolution in traditional consumption patterns and s
34#
發(fā)表于 2025-3-27 12:40:20 | 只看該作者
Public Innovation: Concept and Future Research Agendach designates a collection of approaches for exploring, testing and validating new ideas that create added value for society. Despite its relevance, studies are still needed to go further in analyzing the literature built on the subject, and to identify new research agendas that can generate inputs
35#
發(fā)表于 2025-3-27 14:02:32 | 只看該作者
36#
發(fā)表于 2025-3-27 19:00:21 | 只看該作者
37#
發(fā)表于 2025-3-28 01:32:12 | 只看該作者
38#
發(fā)表于 2025-3-28 03:27:23 | 只看該作者
39#
發(fā)表于 2025-3-28 10:12:19 | 只看該作者
40#
發(fā)表于 2025-3-28 12:42:49 | 只看該作者
Efficient Estimation of Ontology Entities Distributed Representationsntations for ontology entities based on the semantic embedded in ontologies, using a multi-input feed-forward neural network trained using noise contrastive estimation technique. Semantically similar ontology entities will have relatively close corresponding representations in the projection space.
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