Personalized Recommendation Based on Unbalanced Symmetrical Mass Diffusion

Gao, Tianrun; Zhang, Yuexia; Zhu, Xuzhen; Li, Lihua

doi:10.1109/bigmm.2017.23

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…For instance, mass diffusion (MD) [12], heat conduction(HC) [11] and hybrid methods of both (Hyb) [13] are personalized recommendation algorithms based on diffusion processes on bipartite networks [15]. Besides, there are also plenty of extension methods which are designed to improve the recommendation performance, such as indirect-link-weakened mass diffusion method (IMD) [16] ,non-equilibrium mass diffusion algorithm (NMD) [17] and so on [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Predictability of diffusion-based recommender systems

Zhang

Xue

Zeng

2019

Knowledge-Based Systems

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The recommendation methods based on network diffusion have been shown to perform well in both recommendation accuracy and diversity. Nowdays, numerous extensions have been made to further improve the performance of such methods. However, to what extent can items be predicted by diffusion-based algorithms still lack of understanding. Here, we mainly propose a method to quantify the predictability of diffusion-based algorithms. Accordingly, we conduct experiments on Movielens and Netflix datasets. The results show that the higher recommendation accuracy based on diffusion algorithms can still be achieved by optimizing the way of resource allocation on a density network. On a sparse network, the possibility of improving accuracy is relatively low due to the fact that the current accuracy of diffusion-based methods is very close its predictability. In this case, we find that the predictability can be improved significantly by multi-steps diffusion, especially for users with less historical information. In contrast to common belief, there are plausible circumstances where the higher predictability of diffusion-based methods do not correspond to those users with more historical recording. Thus, we proposed the diffusion coverage and item average degree to explain this phenomenon. In addition, we demonstrate the recommendation accuracy in real online system is overestimated by random partition used in the literature, suggesting the recommendation in real online system may be a harder task.

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