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Microblogs recommendations based on implicit similarity in content social networks

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Abstract

With the development of online social networking applications, microblogs have become a necessary online communication network in daily life. Users are interested in obtaining personalized recommendations related to their tastes and needs. In some microblog systems, tags are not available, or the use of tags is rare. In addition, user-specified social relations are extremely rare. Hence, sparsity is a problem in microblog systems. To address this problem, we propose a new framework called Pblog to alleviate sparsity. Pblog identifies users’ interests via their microblogs and social relations and computes implicit similarity among users using a new algorithm. The experimental results indicated that the use of this algorithm can improve the results. In online social networks, such as Twitter, the number of microblogs in the system is high, and it is constantly increasing. Therefore, providing personalized recommendations to target users requires considerable time. To address this problem, the Pblog framework groups similar users using the analytic hierarchy process (AHP) method. Then, Pblog prunes microblogs of the target user group and recommends microblogs with higher ratings to the target user. In the experimental results section, the Pblog framework was compared with several other frameworks. All of these frameworks were run on two datasets: Twitter and Tumblr. Based on the results of these comparisons, the Pblog framework provides more appropriate recommendations to the target user than previous frameworks.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Elham Mazinan & Mitra Mirzarezaee

  2. Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Hassan Naderi

  3. Department of Mathematics, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Saber Saati

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  1. Elham Mazinan
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  2. Hassan Naderi
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  4. Saber Saati
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Correspondence to Hassan Naderi.

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Mazinan, E., Naderi, H., Mirzarezaee, M. et al. Microblogs recommendations based on implicit similarity in content social networks. J Supercomput 78, 962–986 (2022). https://doi.org/10.1007/s11227-021-03864-8

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