Effective mechanism for social recommendation of news

Abstract

a b s t r a c tRecommender 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 the 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 the network's topology almost as fast and effectively as other not-scalable methods that make use of much more information.

Adaptive social recommendation in a multiple category landscape

“…Then users obtain recommendations as a natural result of the information spreading process. The model has been extensively tested by agent-based simulations [13], and additional aspects like users' reputation [14], implicit ratings [15], local topology optimization [16], leadership structure [17] and link reciprocity [18] were subsequently investigated in detail.…”

Enhancing topology adaptation in information-sharing social networks

“…Finally, ∆, D and D A determine how much users are demanding and how wide are their scopes of interest (see [17] for a discussion on how the system's performance relates to these quantities), whereas, u is the frequency for link rewiring, which is set to an optimal value for having both effective recommendations and low computational complexity [19].…”

“…A recently proposed news recommendation model [17][18][19][20] mimics the spreading process typical for social sys- * giulio.cimini@unifr.ch † dbchen@uestc.edu.cn tems and combines it with an adaptive network of connections. In this model, when a user reads a news (or a different kind of content), she can either "approve" or "disapprove" it.…”

An effective trust-based recommendation method using a novel graph clustering algorithm

“…Considering the thousands of products on e-commerce sites, making suitable recommendations which match users' interests is an important problem. Accurate recommendations enable users to quickly find their desirable items without being confused by irrelevant information [1][2][3][4][5][6][7].…”