Quantum mechanics

Delphi method

Similarity (geometry)

TOPSIS

Computer security

Ideal solution

tf–idf

Power (physics)

Data mining

Computer science

Mathematical optimization

Thermodynamics

Physics

Ranking (information retrieval)

Operations research

VIKOR method

Mathematics

Image (mathematics)

Fuzzy logic

Reliability (semiconductor)

Artificial intelligence

Information retrieval

Term (time)

Key (lock)

International Transactions in Operational Research

Nowadays, we can use different websites that help us make decisions about various aspects of our lives. However, privacy protection prevents websites from providing personalised guidelines to users. We propose a novel doctor‐ranking system (DRS) based on multi‐criteria group decision‐making (MCGDM) method to address the problems of privacy protection. The following aspects differentiate our proposed DRS model from previous works: (a) textual information reviews are used to identify user preferences and complementary criteria, (b) criteria weights are determined by term frequency inverse document frequency (TF‐IDF) instead of Delphi method or expert opinion, (c) intuitionistic fuzzy sets (IFSs) are used to replace sentiment analysis to express subjective user criteria, and (d) VIsekriterijumsko KOmpromisno Rangiranjie (VIKOR) method for MCGDM with IFSs is used to solve the doctor‐ranking problem. We apply our proposed model to datasets from Haodf.com to compare the performance of our method with that of sentiment analysis and technique for order performance by similarity to ideal solution (TOPSIS) methods. The experimental results show that our method provides accurate ranking and increases the reliability of DRS.

New doctors ranking system based on VIKOR method