Interest-Based vs. Social Person-Recommenders in Social Networking Platforms

TL;DR

This study empirically compares interest-based and social network-based person recommenders on a large platform, showing interest-based methods produce more novel suggestions, while combined approaches balance novelty and accuracy effectively.

Contribution

It provides an empirical comparison of interest-based and social-based person recommenders, highlighting the benefits of combined approaches in social networking.

Findings

Interest-based recommenders generate more novel suggestions.

Social-based recommenders better reproduce known friendships.

Combined social-interest approaches perform slightly better overall.

Abstract

Social network based approaches to person recommendations are compared to interest based approaches with the help of an empirical study on a large German social networking platform. We assess and compare the performance of different basic variants of the two approaches by precision / recall based performance with respect to reproducing known friendship relations and by an empirical questionnaire based study. In accordance to expectation, the results show that interest based person recommenders are able to produce more novel recommendations while performing less well with respect to friendship reproduction. With respect to the user's assessment of recommendation quality all approaches perform comparably well, while combined social-interest-based variants are slightly ahead in performance. The overall results qualify those combined approaches as a good compromise.