@Article{csse.2022.021525,
AUTHOR = {Donglei Lu, Dongjie Zhu, Haiwen Du, Yundong Sun, Yansong Wang, Xiaofang Li, Rongning Qu, Ning Cao, Russell Higgs},
TITLE = {Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {42},
YEAR = {2022},
NUMBER = {3},
PAGES = {1133--1146},
URL = {http://www.techscience.com/csse/v42n3/46717},
ISSN = {},
ABSTRACT = {The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to the user based on the known historical interaction data of the target user. Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model (<i>CoFM</i>) is one representative research. <i>CoFM</i>, a fusion recommendation model combining the collaborative filtering model <i>FM</i> and the graph embedding model <i>TransE</i>, introduces the information of many entities and their relations in the knowledge graph into the recommendation system as effective auxiliary information. It can effectively improve the accuracy of recommendations and alleviate the problem of sparse user historical interaction data. Unfortunately, the graph-embedded model <i>TransE</i> used in the <i>CoFM</i> model cannot solve the 1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and <i>TransH</i> Model (<i>JFMH</i>) is proposed, which improves <i>CoFM</i> by replacing the <i>TransE</i> model with <i>TransH</i> model. A large number of experiments on two widely used benchmark data sets show that compared with <i>CoFM</i>, <i>JFMH</i> has improved performance in terms of item recommendation and knowledge graph completion, and is more competitive than multiple baseline methods.},
DOI = {10.32604/csse.2022.021525}
}