The Research of Recommendation Algorithm based on Complete Tripartite Graph Model

Autor:	Lin Ju, Zhu Yonghua, Liao Shiwei, Lv Bo, Wu Pin, Gao Honghao
Rok vydání:	2015
Předmět:	Heat spreading General Computer Science Computer science Graph (abstract data type) Social tags Mass diffusion Data mining Recommender system computer.software_genre computer Algorithm Graph model
Zdroj:	International Journal of Hybrid Information Technology. 8:205-214
ISSN:	1738-9968
DOI:	10.14257/ijhit.2015.8.12.14
Popis:	Social tags providing abundant information can stimulate a better recommender system equipped with stronger sense of description and analysis on user’s interest. In this paper, graph-based personalized recommendation techniques have been studied. Complete tripartite graph model was proposed and the user’s interest migration was researched comprehensively, Focusing on the dilemma of accuracy and diversity in recommender system, the mass diffusion algorithm and heat spreading algorithm on complete tripartite graph model were carried out. Then, from the perspective of improving confidence in recommender system, the item-tag joint recommendation mechanism was studied. Experimental results show the effectiveness of the algorithm in this paper.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ff735600b5fc37ef1a1893428ab78546 https://doi.org/10.14257/ijhit.2015.8.12.14 Zobrazit plný text záznamu