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

Abstract

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.

Scaling Out Link Prediction with SNAPLE

“…This score may also exploit vertex content, i.e. the additional application-dependent knowledge attached to vertices [7,16,31,32,39], such as user profiles, tags, or documents. In many domains, pure topological metrics tend to be the main drivers of link generation, and are therefore almost always present in the prediction process.…”