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    Université de Fribourg

    Information filtering in sparse online systems: recommendation via semi-local diffusion

    Zeng, Wei ; Zeng, An ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

    In: PLoS ONE, 2013, vol. 8, no. 11, p. e79354

    With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot...

    Université de Fribourg

    Negative ratings play a positive role in information filtering

    Zeng, Wei ; Zhu, Yu-Xiao ; Lü, Linyuan ; Zhou, Tao

    In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, no. 23-24, p. 4486-4493

    The explosive growth of information asks for advanced information filtering techniques to solve the so-called information overload problem. A promising way is the recommender system which analyzes the historical records of users’ activities and accordingly provides personalized recommendations. Most recommender systems can be represented by user-object bipartite networks where users can...