E-Product Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

2020 ◽  
pp. 38-47
Author(s):  
Jianghai Lv ◽  
Yawen Li ◽  
Junping Du ◽  
Lei Shi
Keyword(s):  
Collaborative Filtering ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Product Recommendation

Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

2020 ◽  
Vol 34 (10) ◽  
pp. 2059033
Author(s):  
Gang Huang ◽  
Man Yuan ◽  
Chun-Sheng Li ◽  
Yong-he Wei
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Cold Start ◽  
Petroleum Exploration ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Exploration And Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

Combining Item Rating Similarity and Item Classification Similarity for Better Recommendation Quality

2012 ◽  
Vol 461 ◽  
pp. 289-292
Author(s):  
Kai Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Fundamental Problem ◽  
The Other ◽  
Recommendation Algorithm ◽  
Data Sparsity ◽  
Filtering Method ◽  
Product Recommendation ◽  
Sparsity Problem

Recommender systems are becoming increasingly popular, and collaborative filtering method is one of the most important technologies in recommender systems. The ability of recommender systems to make correct predictions is fundamentally determined by the quality and fittingness of the collaborative filtering that implements them. It is currently mainly used for business purposes such as product recommendation. Collaborative filtering has two types. One is user based collaborative filtering using the similarity between users to predict and the other is item based collaborative filtering using the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem of data sparsity. This paper gives a personalized collaborative filtering recommendation algorithm combining the item rating similarity and the item classification similarity. This method can alleviate the data sparsity problem in the recommender systems

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 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 A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 50880-50892
Author(s):  
Bo Jiang ◽  
Junchen Yang ◽  
Yanbin Qin ◽  
Tian Wang ◽  
Muchou Wang ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Knowledge Graph ◽  
Service Recommendation ◽  
Recommendation Algorithm
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