Improving recommender system via knowledge graph based exploring user preference

Fan, Huilian; Zhong, Yuanchang; Zeng, Guangpu; Ge, Chenhao

doi:10.1007/s10489-021-02872-8

Cited by 14 publications

(6 citation statements)

References 47 publications

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“…Through the knowledge map, the knowledge mastery of students can be more accurately depicted, and resources can be more accurately depicted. In this way, it can realize accurate research and judgment of users [8], learning path planning, and personalized recommendation of learning resources.…”

Section: Application Logic Of Educational Knowledgementioning

confidence: 99%

Research on the Strategy of Autonomous Learning under the Dual-Class Model of Ideological and Political Courses Based on the Knowledge Map Route

Shi

2022

Wireless Communications and Mobile Computing

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The two-part classroom teaching mode organically combines teachers’ lectures with students’ discussions, which can not only ensure the dominance of ideological and political teachers’ classroom teaching but also give full play to students’ autonomy in learning. The role of students’ autonomous learning as a bridge between teachers’ lectures and students’ discussions cannot be ignored. Through the study of the knowledge map, the self-learning strategies of ideological and political courses calculated from the aspects of resource push and path planning, accurate learning resource search, in-depth reading, and answering robots will maximize the benefits of students’ self-learning.

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Section: Application Logic Of Educational Knowledgementioning

confidence: 99%

Research on the Strategy of Autonomous Learning under the Dual-Class Model of Ideological and Political Courses Based on the Knowledge Map Route

Shi

2022

Wireless Communications and Mobile Computing

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“…The latter is typically employed when a developer wants to "freely experiment with changes without affecting the original project." 9 Moreover, GitHub groups the most popular projects under a curated list, i.e., featured topics. In such a way, the popularity of a repository helps the mining process filter out unuseful artifacts, e.g., toy and dummy projects.…”

Section: Challenges In Mining the Github Ecosystemmentioning

confidence: 99%

“…Fan et al [9] conceived an end-to-end framework to improve recommender systems by means of a knowledge graph, which can capture users' preferences. To this end, a preference matrix (UPM) was built to project refined item embeddings from their latent space into the user embedding space.…”

Section: Boosting Techniques Recommender Systemsmentioning

confidence: 99%

HybridRec: A recommender system for tagging GitHub repositories

et al. 2022

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Software repositories are increasingly essential to support the management of typical artifacts building up projects, including source code, documentation, and bug reports. GitHub is at the forefront of this kind of platforms, providing developer with a reservoir of code contained in more than 28M repositories. To help developers find the right artifacts, GitHub uses topics, which are short texts assigned to the stored artifacts. However, assigning inappropriate topics to a repository might hamper its popularity and reachability. In our previous work, we implemented MNBN and TopFilter to recommend GitHub topics. MNBN exploits a stochastic network to predict topics, while TopFilter relies on a syntactic-based function to recommend topics. In this paper, we extend our work by building HybridRec, a recommender system based on stochastic and collaborative-filtering techniques to generate more relevant topics. To deal with unbalanced datasets, we employ a Complement Naïve Bayesian Network (CNBN). Furthermore, we apply a preprocessing phase to clean and refine the input data before feeding the recommendation engine. An empirical evaluation demonstrates that HybridRec outperforms three state-of-the-art baselines, obtaining a better performance with respect to various metrics. We conclude that the conceived framework can be used to help developers increase their projects’ visibility.

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“…SP-TAG can be combined with existing KGRL methods to improve performance. (3) We conducted experiments on multiple benchmark datasets to compare SP-TAG with methods that only consider triples or integrate text information. e results also show that SP-TAG is more reliable with few training samples because of its augmentation and propagation characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge graphs (KGs) are structured graph databases, usually large in scale, with many entities, relations, and triples. KGs are useful for intelligent search [ 1 ], recommendation systems [ 2 , 3 ], intelligent question answering [ 4 , 5 ], and other applications. Common KGs include Freebase [ 6 ], YAGO [ 7 ], and WordNet [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs

Wang

Zhou

et al. 2022

Computational Intelligence and Neuroscience

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Knowledge graph representation learning aims to provide accurate entity and relation representations for tasks such as intelligent question answering and recommendation systems. Existing representation learning methods, which only consider triples, are not sufficiently accurate, so some methods use external auxiliary information such as text, type, and time to improve performance. However, they often encode this information independently, which makes it challenging to fully integrate this information with the knowledge graph at a semantic level. In this study, we propose a method called SP-TAG, which realizes the semantic propagation on text-augmented knowledge graphs. Specifically, SP-TAG constructs a text-augmented knowledge graph by extracting named entities from text descriptions and connecting them with the corresponding entities. Then, SP-TAG uses a graph convolutional network to propagate semantic information between the entities and new named entities so that the text and triple structure are fully integrated. The results of experiments on multiple benchmark datasets show that SP-TAG attains competitive performance. When the number of training samples is limited, SP-TAG maintains its high performance, verifying the importance of text augmentation and semantic propagation.

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Improving recommender system via knowledge graph based exploring user preference

Cited by 14 publications

References 47 publications

Research on the Strategy of Autonomous Learning under the Dual-Class Model of Ideological and Political Courses Based on the Knowledge Map Route

Research on the Strategy of Autonomous Learning under the Dual-Class Model of Ideological and Political Courses Based on the Knowledge Map Route

HybridRec: A recommender system for tagging GitHub repositories

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs

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