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Data mining-based recommendation system using social networks-an analytical study.

Sahar Ajmal1, Muhammad Awais1, Khaldoon S Khurshid1

  • 1Department of Computer Science, University of Engineering and Technology, Lahore, Punjab, Pakistan.

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Summary
This summary is machine-generated.

This systematic literature review analyzed 42 studies on data mining-based recommendation systems using social networks. Hybrid models combining algorithms show promise for enhancing social media recommendations.

Keywords:
Collaborative filteringContent-based filteringData miningRecommendation systemSocial media

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Area of Science:

  • Data Science
  • Computer Science
  • Information Systems

Background:

  • Social media generates vast amounts of data, posing storage and processing challenges.
  • Efficient data storage and processing are crucial for effective social media recommendations.
  • Data mining techniques are essential for developing robust recommendation systems.

Purpose of the Study:

  • To systematically review and analyze studies on data mining-based recommendation systems using social networks (DRSN) from 2011 to 2021.
  • To identify trends in publication venues, geographical distribution, and methodologies.
  • To highlight limitations and suggest future research directions for enhancing DRSN.

Main Methods:

  • A systematic literature review (SLR) following Kitchenham's methodology.
  • Analysis of 42 selected studies based on predefined research questions.
  • Examination of publication details, approaches, datasets, and evaluation metrics.

Main Results:

  • Most publications (21%) were in 2018, with China leading (40%) in contributions.
  • IEEE is the dominant publication venue (61%).
  • Collaborative filtering (21%) and Twitter datasets (19%) are frequently used, with precision and recall as key metrics (28%).

Conclusions:

  • Current DRSN studies have limitations, indicating a need for more sophisticated approaches.
  • Hybrid models, integrating diverse algorithms, are recommended for improved social media recommendations.
  • Future research should focus on developing and evaluating hybrid models for enhanced user experience.