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Unknown but interesting recommendation using social penetration

  • Jen-Wei Huang
  • Hao-Shang Ma
  • Chih-Chin Chung
  • Zhi-Jia Jian
Methodologies and Application

Abstract

With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively.

Keywords

Social network analysis Recommendation system Unknown but interesting 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Institute of Computer and Communication EngineeringNational Cheng Kung UniversityTainan CityTaiwan
  2. 2.Department of Computer Science and EngineeringYuan Ze UniversityTaoyuan CityTaiwan

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