scholarly journals Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Gongwen Xu ◽  
Guangyu Jia ◽  
Lin Shi ◽  
Zhijun Zhang
Keyword(s):  
Online Education ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Semantic Information ◽  
Representation Learning ◽  
Semantic Space ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Semantic Level

Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners’ enthusiasm and give full play to different learners’ learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.

scholarly journals Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ruihui Mu ◽  
Xiaoqin Zeng
Keyword(s):  
Collaborative Filtering ◽  
Semantic Information ◽  
Representation Learning ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Semantic Level ◽  
Filtering Algorithm ◽  
Semantic Data ◽  
Collaborative Filtering Algorithm

To solve the problem that collaborative filtering algorithm only uses the user-item rating matrix and does not consider semantic information, we proposed a novel collaborative filtering recommendation algorithm based on knowledge graph. Using the knowledge graph representation learning method, this method embeds the existing semantic data into a low-dimensional vector space. It integrates the semantic information of items into the collaborative filtering recommendation by calculating the semantic similarity between items. The shortcoming of collaborative filtering algorithm which does not consider the semantic information of items is overcome, and therefore the effect of collaborative filtering recommendation is improved on the semantic level. Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.

scholarly journals An Efficient Knowledge-Graph-Based Web Service Recommendation Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 392 ◽  
Author(s):  
Zhiying Cao ◽  
Xinghao Qiao ◽  
Shuo Jiang ◽  
Xiuguo Zhang
Keyword(s):  
Web Service ◽  
Semantic Information ◽  
Dimensional Space ◽  
Representation Learning ◽  
Recall Rate ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Service Recommendation ◽  
Recommendation Algorithm ◽  
Low Dimensional

Using semantic information can help to accurately find suitable services from a variety of available (different semantics) services, and the semantic information of Web services can be described in detail in a Web service knowledge graph. In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed. The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. By calculating the distance between service entities in low-dimensional space, the relationship information of services which is not considered in recommendation approaches using a collaborative filtering algorithm is incorporated into the recommendation algorithm to enhance the accurateness of the result. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate, and coverage rate of recommendation but also solve the cold start problem to some extent.

scholarly journals Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jing Li ◽  
Zhou Ye
Keyword(s):  
Online Education ◽  
Collaborative Filtering ◽  
Online Teaching ◽  
High Performance ◽  
Recommendation System ◽  
Mobile Terminal ◽  
Programming Approach ◽  
Recommendation Algorithm ◽  
Server Side ◽  
Education Platform

In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online.

scholarly journals Hybrid high-order semantic graph representation learning for recommendations

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Canta Zheng ◽  
Wenming Cao
Keyword(s):  
Collaborative Filtering ◽  
Semantic Information ◽  
Information Overload ◽  
Rapid Development ◽  
Representation Learning ◽  
High Order ◽  
Graph Representation ◽  
Model Based ◽  
Recommendation Algorithms ◽  
Low Dimensional

AbstractThe amount of Internet data is increasing day by day with the rapid development of information technology. To process massive amounts of data and solve information overload, researchers proposed recommender systems. Traditional recommendation methods are mainly based on collaborative filtering algorithms, which have data sparsity problems. At present, most model-based collaborative filtering recommendation algorithms can only capture first-order semantic information and cannot process high-order semantic information. To solve the above issues, in this paper, we propose a hybrid graph neural network model based on heterogeneous graphs with high-order semantic information extraction, which models users and items respectively by learning low-dimensional representations for them. We introduced an attention mechanism to allow the model to understand the corresponding edge weights adaptively. Simultaneously, the model also integrates social information in the data to learn more abundant information. We performed some experiments on related datasets. Our method achieved better results than some current advanced models, which verified the proposed model’s effectiveness.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hui Ning ◽  
Qian Li
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Contextual Information ◽  
Rapid Development ◽  
Theoretical Research ◽  
Context Information ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Points Of Interest ◽  
Popularity Prediction

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

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