A Collaborative Filtering Model for Link Prediction of Fusion Knowledge Graph
| Autor: | Wenqian Shang, Zaifu Yu, Wei Huang, Weiguo Lin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Knowledge engineering 02 engineering and technology Link (geometry) computer.software_genre Interaction information Data modeling Data set Matrix (mathematics) 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Data mining computer Sparse matrix |
| Zdroj: | SNPD Winter |
| DOI: | 10.1109/snpdwinter52325.2021.00016 |
| Popis: | In order to solve the problem that collaborative filtering recommendation algorithm completely depends on the interactive behavior information of users while ignoring the correlation information between items, this paper introduces a link prediction algorithm based on knowledge graph to integrate ItemCF algorithm. Through the linear weighted fusion of the item similarity matrix obtained by the ItemCF algorithm and the item similarity matrix obtained by the link prediction algorithm, the new fusion matrix is then introduced into ItemCF algorithm. The MovieLens-1M data set is used to verify the KGLP-ItemCF model proposed in this paper, and the experimental results show that the KGLP-ItemCF model effectively improves the precision, recall rate and F1 value. KGLP-ItemCF model effectively solves the problems of sparse data and over-reliance on user interaction information by introducing knowledge graph into ItemCF algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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