In: Physical Review E - Statistical Nonlinear, and Soft Matter Physics, 2011, vol. 83, no. 6, p. 066119
Recommender systems have shown great potential in addressing the information overload problem, namely helping users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including the heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus...
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In: BMC Ecology, 2011, vol. 11, p. 14
Background Regular seasonal changes in prevalence of infectious diseases are often observed in nature, but the mechanisms are rarely understood. Empirical tests aiming at a better understanding of seasonal prevalence patterns are not feasible for most diseases and thus are widely lacking. Here, we set out to study experimentally the seasonal prevalence in an aquatic host-parasite system. The...
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In: International Journal of Modern Physics C, 2010, vol. 21, no. 6, p. 813-824
Two main difficulties in the problem of classification in partially labeled networks are the sparsity of the known labeled nodes and inconsistency of label information. To address these two difficulties, we propose a similarity-based method, where the basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten...
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In: Physics Procedia, 2010, vol. 3, no. 5, p. 1887-1896
Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson...
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In: Europhysics Letters, 2010, vol. 90, no. 4, p. 48006
Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com (http://audioscrobbler.com/) and del.icio.us (http://del.icio.us/), where users are connected with music groups and bookmarks, respectively. The degree...
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In: Chinese Physics Letters, 2010, vol. 27, no. 4, p. 048701
Empirical observations indicate that the interevent time distribution of human actions exhibits heavy-tailed features. The queuing model based on task priorities is to some extent successful in explaining the origin of such heavy tails, however, it cannot explain all the temporal statistics of human behavior especially for the daily entertainments. We propose an interest-driven model, which can...
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In: Proceedings of the National Academy of Sciences of the USA - PNAS, 2010, vol. 107, no. 10, p. 4511-4515
Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to...
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In: Europhysics Letters, 2009, vol. 88, no. 6, p. 68008
In some recommender systems where users can vote objects by ratings, the similarity between users can be quantified by a benchmark index, namely the Pearson correlation coefficient, which reflects the rating correlations. Another alternative way is to calculate the similarity based solely on the relevance information, namely whether a user has voted an object. The former one uses more information...
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In: Chinese Physics Letters, 2009, vol. 26, no. 11, p. 118903
Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and therefore have the potential to help in improving better personalized recommendations. We propose a diffusion-based recommendation algorithm considering the personal vocabulary...
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In: Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, no. 23, p. 4867-4871
In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain...
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