TY - JOUR
T1 - A novel method on information recommendation via hybrid similarity
AU - Zhao, Qin
AU - Wang, Cheng
AU - Wang, Pengwei
AU - Zhou, Mengchu
AU - Jiang, Changjun
N1 - Funding Information:
Manuscript received October 22, 2015; revised August 5, 2016; accepted November 14, 2016. Date of publication December 21, 2016; date of current version February 14, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 91218301, Grant 61602109, and Grant 61572326, in part by the Shanghai Science and Technology Innovation Action Plan under Grant 16511100900, in part by the Shanghai Sailing Program under Grant 16YF1400300, and in part by FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia) under Grant 119/2014/A3. This paper was recommended by Associate Editor L. Cao. (Corresponding author: Changjun Jiang.) Q. Zhao is with the College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China, and also with the Key Laboratory of Embedded System and Service Computing of Ministry of Education, Tongji University, Shanghai 200092, China (e-mail: q_zhao@shnu.edu.cn).
Publisher Copyright:
© 2016 IEEE.
PY - 2018/3
Y1 - 2018/3
N2 - Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate.
AB - Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate.
KW - Data mining
KW - Information recommendation
KW - Information retrieval
KW - Link similarity
UR - http://www.scopus.com/inward/record.url?scp=85044577256&partnerID=8YFLogxK
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U2 - 10.1109/TSMC.2016.2633573
DO - 10.1109/TSMC.2016.2633573
M3 - Article
AN - SCOPUS:85044577256
SN - 2168-2216
VL - 48
SP - 448
EP - 459
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 3
M1 - 7792740
ER -