Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Mu, Ruihui; Zeng, Xin

doi:10.1155/2018/9617410

Cited by 20 publications

(14 citation statements)

References 34 publications

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“…For the fitness estimation strategies, there are two main categories of research. Methods, such as elite set [19], directed graph [20], grey model [21], fuzzy range [22], and maximum entropy criterion [23], have been proposed. The other category involves applying the fitness function approximation model.…”

Section: Related Workmentioning

confidence: 99%

Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Zhu

Pan

et al. 2019

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To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.

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Section: Related Workmentioning

confidence: 99%

Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Zhu

Pan

et al. 2019

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“…For example, some users might provide more ratings on a few items as compared to other items. The results of a classifier can also be represented using the "confusion matrix [17]. The matrix is a two-dimensional table with a row and column for each class.…”

Section: A Offline Experimentsmentioning

confidence: 99%

Selecting Appropriate Metrics for Evaluation of Recommender Systems

Rawat¹,

Dwivedi²

2019

IJITCS

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The abundance of information on the web makes it difficult for users to find items that meet their information need effectively. To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and egovernment among others. Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task. However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.

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“…Most of the collaborative filtering based recommendation approaches only use the user-item rating matrix and do not consider semantic information. Ruihui Mu and Xiaoqin Zeng proposed an approach based on a knowledge-graph to solve this issue [25]. They have used representation learning mechanisms on knowledge graphs to embed existing semantics into a vector space which is low-dimensional.…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

“…They have used representation learning mechanisms on knowledge graphs to embed existing semantics into a vector space which is low-dimensional. Their proposed approach combines the semantics of items into the recommendation task by computing the meaning-aware similarity between items [25]. The authors have claimed that their proposed approach could significantly outperform some of the state-of-the-art approach in knowledge graph-based recommendation engines.…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

IJMLNCE Editorial Note Volume No 03, Issue No 03

Solanki¹,

Díaz²

2019

IJMLNCE

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peer-reviewed, international journal, focuses on publishing authentic and unpublished quality research papers in the emerging field of Machine Learning and Collaborative Engineering. This is a scientific journal aims to promote highly qualitative and innovative research works, not limited to but focuses on the area of machine learning, collaborative engineering, and allied areas. We publish only original research articles, review articles, and technical notes.

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Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Cited by 20 publications

References 34 publications

Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Selecting Appropriate Metrics for Evaluation of Recommender Systems

IJMLNCE Editorial Note Volume No 03, Issue No 03

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