Effective Mechanism for Social Recommendation of News

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.

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Cited by in corpus (7)

• Recommender Systems
• Small world yields the most effective information spreading
• Emergence of scale-free leadership structure in social recommender systems
• Enhancing topology adaptation in information-sharing social networks
• Adaptive social recommendation in a multiple category landscape
• Impacts of suppressing guide on information spreading
• The role of taste affinity in agent-based models for social recommendation