一种使用网络嵌入方法的知识驱动的纸质推荐方法
发布时间：2022-08-10 20:55
日常生活中,我们经常在互联网上搜索各种各样的东西,而且有很多搜索引擎能够为我们搜索到相关的结果。随着技术的飞速发展,互联网已变成了人们获取信息的主要来源。此外,Web2.0时代的到来使得用户和网站之间的交互增加了。依据用户兴趣为用户提供信息,变得具有挑战性。然而由于版权的限制,现有的大多数研究都面临着缺乏候选推荐文章内容的问题。这些文章的内容并不都是可以免费获得的,这导致了推荐结果不充分。此外,很多研究是基于推荐用户的个人资料的。因此,他们的推荐需要系统中有大量的注册用户。近年来,研究工作证明知识图在产生高质量推荐结果、减轻稀疏性和冷启动问题方面取得了更好的效果。网络嵌入技术尝试从网络结构中自动地学习高质量特征向量,使得基于向量的节点相关性度量成为可能。保持网络嵌入技术的优势,本文提出了知识驱动的论文推荐方法,利用异构网络嵌入模型来生成推荐结果。本文的创新性在于利用网络嵌入方法的性能,即matapath2vec,此方法在学习知识网络并找到满足用户需求的论文以及识别并整合论文中的潜在关系方面,可以发挥重要作用,这可以帮助改善推荐的结果。与现有方法不同,本文所提出的方法具有学习网络中节点（...
【文章页数】：64 页
【学位级别】：硕士
【文章目录】：
摘要
Abstract
Abbreviations& Acronyms
Chapter1 Introduction
1.1 Background
1.2 Research background and research significance
1.3 Research content and contribution
1.4 Organization of the thesis
Chapter2 Related Work
2.1 Traditional recommendation Algorithm
2.2 A content-based Paper Recommender System
2.3 Knowledge Graph Embeddings Based Recommendation Using Node2vec
2.4 Recommendation model based on knowledge network representation
2.5 Summary
Chapter3 The KN-HER Model
3.1 Problem formulation
3.2 Problem Definition
3.3 Architecture and working of the proposed method
3.4 Model Overview
3.4.1 Construction of Heterogeneous Papers Network
3.4.2 Heterogeneous Papers Network Embedding
3.4.3 KN-HER Model based Citation Recommendation Approach
3.5 Cold-start and Sparsity scenario in paper recommendation
3.6 Summary
Chapter4 Performance Evaluation and Results
4.1 Experimental settings
4.1.1 Implementation plateform
4.1.2 Implemented Dataset
4.2 Evaluation Metrics
4.3 Evaluation Results
4.3.1 Analysis on the basis of Precision
4.3.2 Analysis on The Basis of Recall
4.3.3 Analysis on The Basis of NDCG
4.4 Case Studies
4.5 Summary
Chapter5 System Design and Implementation
5.1 System framework and design
5.1.1 System Framework
5.1.2 System Functions
5.2 Data acquisition
5.3 System implementation and function display
5.3.1 System Implementation
5.4 Summary
Chapter6 Conclusion and Future Work
6.1 Conclusion
6.2 Future work
Acknowledgement
References
本文编号：3674344
【文章页数】：64 页
【学位级别】：硕士
【文章目录】：
摘要
Abstract
Abbreviations& Acronyms
Chapter1 Introduction
1.1 Background
1.2 Research background and research significance
1.3 Research content and contribution
1.4 Organization of the thesis
Chapter2 Related Work
2.1 Traditional recommendation Algorithm
2.2 A content-based Paper Recommender System
2.3 Knowledge Graph Embeddings Based Recommendation Using Node2vec
2.4 Recommendation model based on knowledge network representation
2.5 Summary
Chapter3 The KN-HER Model
3.1 Problem formulation
3.2 Problem Definition
3.3 Architecture and working of the proposed method
3.4 Model Overview
3.4.1 Construction of Heterogeneous Papers Network
3.4.2 Heterogeneous Papers Network Embedding
3.4.3 KN-HER Model based Citation Recommendation Approach
3.5 Cold-start and Sparsity scenario in paper recommendation
3.6 Summary
Chapter4 Performance Evaluation and Results
4.1 Experimental settings
4.1.1 Implementation plateform
4.1.2 Implemented Dataset
4.2 Evaluation Metrics
4.3 Evaluation Results
4.3.1 Analysis on the basis of Precision
4.3.2 Analysis on The Basis of Recall
4.3.3 Analysis on The Basis of NDCG
4.4 Case Studies
4.5 Summary
Chapter5 System Design and Implementation
5.1 System framework and design
5.1.1 System Framework
5.1.2 System Functions
5.2 Data acquisition
5.3 System implementation and function display
5.3.1 System Implementation
5.4 Summary
Chapter6 Conclusion and Future Work
6.1 Conclusion
6.2 Future work
Acknowledgement
References
本文编号：3674344
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