Multidimensional Social Network in the Social Recommender System

TL;DR

This paper introduces a multidimensional social network model based on layered relationships derived from user interactions and shared objects, and applies it to enhance personalized social recommendations in multimedia sharing platforms.

Contribution

It proposes a novel layered social network framework with specific strength measures and demonstrates its effectiveness in improving social recommendations.

Findings

Relationships stem from semantic and social links.

Network density increases over time.

Personalized recommendations benefit from layered network insights.

Abstract

All online sharing systems gather data that reflects users' collective behaviour and their shared activities. This data can be used to extract different kinds of relationships, which can be grouped into layers, and which are basic components of the multidimensional social network proposed in the paper. The layers are created on the basis of two types of relations between humans, i.e. direct and object-based ones which respectively correspond to either social or semantic links between individuals. For better understanding of the complexity of the social network structure, layers and their profiles were identified and studied on two, spanned in time, snapshots of the Flickr population. Additionally, for each layer, a separate strength measure was proposed. The experiments on the Flickr photo sharing system revealed that the relationships between users result either from semantic links between objects they operate on or from social connections of these users. Moreover, the density of the social network increases in time. The second part of the study is devoted to building a social recommender system that supports the creation of new relations between users in a multimedia sharing system. Its main goal is to generate personalized suggestions that are continuously adapted to users' needs depending on the personal weights assigned to each layer in the multidimensional social network. The conducted experiments confirmed the usefulness of the proposed model.