Effective Mechanism for Social Recommendation of News

TL;DR

This paper presents scalable stochastic algorithms for social news recommendation systems that adapt user networks to improve news traffic distribution without requiring explicit user ratings.

Contribution

It introduces novel scalable algorithms for social recommendation networks, enhancing topology optimization efficiency compared to existing methods.

Findings

Algorithms effectively optimize network topology

Improved algorithms nearly match non-scalable methods

Simulations demonstrate scalability and performance

Abstract

Recommendation systems represent an important tool for news distribution on the Internet. In this work we modify a recently proposed social recommendation model in order to deal with no explicit ratings of users on news. The model consists of a network of users which continually adapts in order to achieve an efficient news traffic. To optimize network's topology we propose different stochastic algorithms that are scalable with respect to the network's size. Agent-based simulations reveal the features and the performance of these algorithms. To overcome the resultant drawbacks of each method we introduce two improved algorithms and show that they can optimize network's topology almost as fast and effectively as other not-scalable methods that make use of much more information.