Spiral of Silence and Its Application in Recommender Systems

Chen Lin, Dugang Liu, Yanghua Xiao, Hanghang Tong

Research output: Contribution to journalArticlepeer-review

Abstract

It is crucial to model missing ratings in recommender systems since user preferences learnt from only observed ratings are biased. One possible explanation for missing ratings is motivated by the spiral of silence theory. When the majority opinion is formed, a spiral process is triggered where users are more and more likely to show their ratings if they perceive that they are supported by the opinion climate. In this paper we first verify the existence of the spiral process in recommender systems by using a variety of different real-life datasets. We then study the characteristics of two key factors in the spiral process: opinion climate and the hardcore users who will give ratings even when they are minority opinion holders. Based on our empirical findings, we develop four variants to model missing ratings. They mimic different components of the spiral of silence based on the spiral process with global opinion climate, local opinion climate, hardcore users, relationships between hardcore users and items, respectively. We experimentally show that, the presented variants all outperform state-of-the-art recommendation models with missing rating components.

Original languageEnglish (US)
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
StateAccepted/In press - 2020
Externally publishedYes

Keywords

  • Computational modeling
  • Hardcore
  • Market research
  • Meteorology
  • Missing not at Random
  • Opinion Climate
  • Probabilistic logic
  • Recommender System
  • Recommender systems
  • Spiral of Silence
  • Spirals

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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