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Making recommendations by integrating information from multiple social networks

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Abstract

It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted experiments have shown that using multiple features from multiple sources is expected to produce a more concrete and wider perspective of user’s behavior and preferences. This leads to improved recommendation outcome.

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Notes

  1. https://imdb.codeplex.com/

  2. We plan to share the collected data-set for academic research.

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Acknowledgments

This research is supported by TUBITAK-BIDEB 2214/A program.

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Authors and Affiliations

  1. Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Makbule Gulcin Ozsoy & Faruk Polat

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  3. Department of Computer Science, Global University, Beirut, Lebanon

    Reda Alhajj

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  1. Makbule Gulcin Ozsoy
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  2. Faruk Polat
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Correspondence to Reda Alhajj.

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Ozsoy, M.G., Polat, F. & Alhajj, R. Making recommendations by integrating information from multiple social networks. Appl Intell 45, 1047–1065 (2016). https://doi.org/10.1007/s10489-016-0803-1

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