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CSD: A Multi-User Similarity Metric for Community Recommendation in Online Social Networks

Xiao Han, Ángel Cuevas, Rubén Cuevas, Noel Crespi

DOI: 10.1016/j.eswa.2016.01.003

Appeared in Expert Systems with Applications, January 2016

Abstract

Communities are basic components in networks. As a promising social application, community recommendation selects a few items (e.g., movies and books) to recommend to a group of users. It usually achieves higher recommendation precision if the users share more interests; whereas, in plenty of communities (e.g., families, work groups), the users often share few. With billions of communities in online social networks, quickly selecting the communities where the members are similar in interests is a prerequisite for community recommendation. To this end, we propose an easy-to-compute metric,Community Similarity Degree (CSD), to estimate the degree of interest similarity among multiple users in a community. Based on 3460 emulated Facebook communities, we conduct extensive empirical studies to reveal the characteristics of CSD and validate the effectiveness of CSD. In particular, we demonstrate that selecting communities with larger CSD can achieve higher recommendation precision. In addition, we verify the computation efficiency of CSD: it costs less than 1 hour to calculate CSD for over 1 million of communities. Finally, we draw insights about feasible extensions to the definition of CSD, and point out the practical uses of CSD in a variety of applications other than community recommendation.

Research highlights:
  • In this paper we develop Facebook crawling techniques which inspired us to latter implement the crawling techniques which reside in the core of TYPES tools such as the FDVT or the Price Comparator.
  • Using the retrieve data with our crawling methodology we develop a similarity metric which allows to recommend content in Facebook for users by grouping them based on the interests they have declared in Facebook. The obtained results shows that this metric is very promising for this purpose.

Read the entire paper here.

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