Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders - Distributed Applications and Interoperable Systems Access content directly
Conference Papers Year : 2015

Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders

Abstract

Recommending appropriate content and users is a critical feature of on-line social networks. Computing accurate recommendations on very large datasets can however be particularly costly in terms of resources , even on modern parallel and distributed infrastructures. As a result, modern recommenders must generally trade-off quality and computational cost to reach a practical solution. This trade-off has however so far been largely left unexplored by the research community, making it difficult for practitioners to reach informed design decisions. In this paper, we investigate to which extent the additional computing costs of advanced recommendation techniques based on supervised classifiers can be balanced by the gains they bring in terms of quality. In particular , we compare these recommenders against their unsupervised counterparts , which offer lightweight and highly scalable alternatives. We propose a thorough evaluation comparing 11 classifiers against 7 lightweight recommenders on a real Twitter dataset. Additionally, we explore data grouping as a method to reduce computational costs in a distributed setting while improving recommendation quality. We demonstrate how classifiers trained using data grouping can reduce their computing time by 6 while improving recommendations up to 22% when compared with lightweight solutions.
Fichier principal
Vignette du fichier
summary.pdf (645.1 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01170757 , version 1 (02-07-2015)

Identifiers

Cite

Anne-Marie Kermarrec, François Taïani, Juan Manuel Tirado Martin. Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders. 15th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), IFIP, Jun 2015, Grenoble, France. pp.138-151, ⟨10.1007/978-3-319-19129-4_11⟩. ⟨hal-01170757⟩
612 View
172 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More