Integration of Link and Semantic Relations for Information Recommendation


  • Qin Zhao The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai
  • Yuan He Tongji University
  • Changjun Jiang Tongji University
  • Pengwei Wang Donghua University
  • Man Qi Canterbury Christ Church University
  • Maozhen Li Brunel University, London


Information retrieval, data mining, link similarity, information recommendation


Information services on the Internet are being used as an important tool to facilitate discovery of the information that is of user interests. Many approaches have been proposed to discover the information on the Internet, while the search engines are the most common ones. However, most of the current approaches of information discovery can discover the keyword-matching information only but cannot recommend the most recent and relative information to users automatically. Sometimes users can give only a fuzzy keyword instead of an accurate one. Thus, some desired information would be ignored by the search engines. Moreover, the current search engines cannot discover the latent but logically relevant information or services for users. This paper measures the semantic-similarity and link-similarity between keywords. Based on that, it introduces the concept of similarity of web pages, and presents a method for information recommendation. The experimental evaluation and comparisons with the existing studies are finally performed.


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How to Cite

Zhao, Q., He, Y., Jiang, C., Wang, P., Qi, M., & Li, M. (2016). Integration of Link and Semantic Relations for Information Recommendation. COMPUTING AND INFORMATICS, 35(1), 30–54. Retrieved from

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