arXiv subjects

Tao Zhou

Publications and source records attributed to Tao Zhou.

At least 505 records · Page 28Linked to original sources

arXiv2010

Solving the Cold-Start Problem in Recommender Systems with Social Tags

In this paper, based on the user-tag-object tripartite graphs, we propose a recommendation algorithm, which considers social tags as an important role for information retrieval. Besides its low cost of computational time, the experiment results of two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity. Especially, it can obtain more personalized recommendation results when users have diverse topics of tags. In addition, the numerical results on the dependence of algorithmic accuracy indicates that the proposed algorithm is particularly effective for small degree objects, which reminds us of the well-known \emph{cold-start} problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree distributions.

cs.IR↗

arXiv2010

Zipf's Law Leads to Heaps' Law: Analyzing Their Relation in Finite-Size Systems

Background: Zipf's law and Heaps' law are observed in disparate complex systems. Of particular interests, these two laws often appear together. Many theoretical models and analyses are performed to understand their co-occurrence in real systems, but it still lacks a clear picture about their relation. Methodology/Principal Findings: We show that the Heaps' law can be considered as a derivative phenomenon if the system obeys the Zipf's law. Furthermore, we refine the known approximate solution of the Heaps' exponent provided the Zipf's exponent. We show that the approximate solution is indeed an asymptotic solution for infinite systems, while in the finite-size system the Heaps' exponent is sensitive to the system size. Extensive empirical analysis on tens of disparate systems demonstrates that our refined results can better capture the relation between the Zipf's and Heaps' exponents. Conclusions/Significance: The present analysis provides a clear picture about the relation between the Zipf's law and Heaps' law without the help of any specific stochastic model, namely the Heaps' law is indeed a derivative phenomenon from Zipf's law. The presented numerical method gives considerably better estimation of the Heaps' exponent given the Zipf's exponent and the system size. Our analysis provides some insights and implications of real complex systems, for example, one can naturally obtained a better explanation of the accelerated growth of scale-free networks.

physics.data-an↗

arXiv2010

Solving the apparent diversity-accuracy dilemma of recommender systems

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 address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.

cs.IR↗

arXiv2010

Spin dynamics in electron-doped pnictide superconductors

The doping dependence of spin excitations in Ba(Fe$_{1-x}$Co$_{x}$)$_{2}$As$_{2}$ is studied based on a two-orbital model under RPA approximation. The interplay between the spin-density-wave (SDW) and superconductivity (SC) is considered in our calculation. Our results for the spin susceptibility are in good agreement with neutron scattering (NS) experiments in various doping ranges at temperatures (T) above and below the superconducting transition temperature T$_{c}$. For the overdoped sample where one of the two hole pockets around $Γ$ point disappears according to ARPES, we show that the imaginary part of the spin susceptibility in both SC and normal phases exhibits a gap-like behavior. This feature is consistent with the "pseudogap" as observed by recent NMR and NS experiments.

cond-mat.supr-con↗

arXiv2010

Spin-Density-Wave and Asymmetry of Coherence Peaks in iron-Pnictide Superconductors from a two-orbital model

We study theoretically the coexistence of the spin-density-wave (SDW) and superconductivity in electron-doped iron-pnictide superconductors based on the two orbital model and Bogoliubov-de Gennes equations. The phase diagram is mapped out and the evolution of the Fermi surface as the doping varies is presented. The local density of states (LDOS) has also been calculated from low to high doping. We show that the strength of the superconducting coherent peak at the positive energy gets enhanced and the one at the negative energy is suppressed by the SDW order in the underdoped region. Several features of our results are in good agreement with the experiments.

cond-mat.supr-con↗

arXiv2010

Building reputation systems for better ranking

How to rank web pages, scientists and online resources has recently attracted increasing attention from both physicists and computer scientists. In this paper, we study the ranking problem of rating systems where users vote objects by discrete ratings. We propose an algorithm that can simultaneously evaluate the user reputation and object quality in an iterative refinement way. According to both the artificially generated data and the real data from MovieLens and Amazon, our algorithm can considerably enhance the ranking accuracy. This work highlights the significance of reputation systems in the Internet era and points out a way to evaluate and compare the performances of different reputation systems.

cs.IR↗

arXiv2009

Adaptive information filtering for dynamic recommender systems

The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the proposed algorithms do not cumulate over time, that is to say, the global recomputing is unnecessary. This remarkable advantage is demonstrated by several metrics on algorithmic accuracy for two movie recommender systems and a social bookmarking system.

cs.IR↗

arXiv2009

Impurity effect as a probe for order parameter symmetry in iron-based superconductors

The pointlike impurity scattering is studied for a superconductor with different pairing symmetries based on a minimal two band model proposed by S. Raghu $et$ $al.$ [Phys. Rev. B {\bf 77}, R220503 (2008)], and the self-consistent Bogoliubov-de Gennes equations. Two intra-gap resonance peaks are found at or near the nonmagnetic impurity site for a typical $s_{x^2y^2}$ symmetry and strong scattering potentials. Such intra-gap resonance peaks are absent for the sign unchanged s-wave and $d_{x^2-y^2}$-wave order parameter. Thus we propose that a non-magnetic impurity with strong scattering potentials can be used to probe the pairing symmetry in iron-based superconductors.

cond-mat.supr-con↗

arXiv2009

Adaptive model for recommendation of news

Most news recommender systems try to identify users' interests and news' attributes and use them to obtain recommendations. Here we propose an adaptive model which combines similarities in users' rating patterns with epidemic-like spreading of news on an evolving network. We study the model by computer agent-based simulations, measure its performance and discuss its robustness against bias and malicious behavior. Subject to the approval fraction of news recommended, the proposed model outperforms the widely adopted recommendation of news according to their absolute or relative popularity. This model provides a general social mechanism for recommender systems and may find its applications also in other types of recommendation.

cs.IR↗

arXiv2009

Collaborative filtering with diffusion-based similarity on tripartite graphs

Collaborative tags are playing more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his preference and tagging information. Two kinds of similarities between users are calculated by using a diffusion-based process, which are then integrated for recommendation. We test the proposed method in a standard collaborative filtering framework with three metrics: ranking score, Recall and Precision, and demonstrate that it performs better than the commonly used cosine similarity.

cs.IR↗

arXiv2009

Highly accurate recommendation algorithm based on high-order similarities

In this Letter, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the standard Pearson coefficient, the user-user similarities are obtained by a diffusion process. Furthermore, by considering the second order similarities, we design an effective algorithm that depresses the influence of mainstream preferences. The corresponding algorithmic accuracy, measured by the ranking score, is further improved by 24.9% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the algorithm based on second order similarity can outperform the MCF simultaneously in all three criteria.

physics.data-an↗

arXiv2009

Effect of user tastes on personalized recommendation

In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the users' tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score, more importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the users' tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes.

physics.data-an↗

arXiv2009

Synchronization on Effective Networks

The study of network synchronization has attracted increasing attention recently. In this paper, we strictly define a class of networks, namely effective networks, which are synchronizable and orientable networks. We can prove that all the effective networks with the same size have the same spectra, and are of the best synchronizability according to the master stability analysis. However, it is found that the synchronization time for different effective networks can be quite different. Further analysis show that the key ingredient affecting the synchronization time is the maximal depth of an effective network: the larger depth results in a longer synchronization time. The secondary factor is the number of links. The more links connecting the nodes in the same layer (horizontal links) will lead to longer synchronization time, while the increasing number of links connecting nodes in neighboring layers (vertical links) will accelerate the synchronization. Our findings provide insights into the roles of horizontal and vertical links in synchronizing process, and suggest that the spectral analysis is helpful yet insufficient for the understanding of network synchronization.

cond-mat.stat-mech↗

arXiv2009

Effective and Efficient Similarity Index for Link Prediction of Complex Networks

Predictions of missing links of incomplete networks like protein-protein interaction networks or very likely but not yet existent links in evolutionary networks like friendship networks in web society can be considered as a guideline for further experiments or valuable information for web users. In this paper, we introduce a local path index to estimate the likelihood of the existence of a link between two nodes. We propose a network model with controllable density and noise strength in generating links, as well as collect data of six real networks. Extensive numerical simulations on both modeled networks and real networks demonstrated the high effectiveness and efficiency of the local path index compared with two well-known and widely used indices, the common neighbors and the Katz index. Indeed, the local path index provides competitively accurate predictions as the Katz index while requires much less CPU time and memory space, which is therefore a strong candidate for potential practical applications in data mining of huge-size networks.

physics.data-an↗

arXiv2009

Scaling and memory in recurrence intervals of Internet traffic

By studying the statistics of recurrence intervals, $τ$, between volatilities of Internet traffic rate changes exceeding a certain threshold $q$, we find that the probability distribution functions, $P_{q}(τ)$, for both byte and packet flows, show scaling property as $P_{q}(τ)=\frac{1}{\overlineτ}f(\fracτ{\overlineτ})$. The scaling functions for both byte and packet flows obeys the same stretching exponential form, $f(x)=A\texttt{exp}(-Bx^β)$, with $β\approx 0.45$. In addition, we detect a strong memory effect that a short (or long) recurrence interval tends to be followed by another short (or long) one. The detrended fluctuation analysis further demonstrates the presence of long-term correlation in recurrence intervals.

physics.data-an↗

arXiv2009

Origin of the Scaling Law in Human Mobility: Hierarchical Organization of Traffic Systems

Uncovering the mechanism leading to the scaling law in human trajectories is of fundamental importance in understanding many spatiotemporal phenomena. We propose a hierarchical geographical model to mimic the real traffic system, upon which a random walker will generate a power-law travel displacement distribution with exponent -2. When considering the inhomogeneities of cities' locations and attractions, this model reproduces a power-law displacement distribution with an exponential cutoff, as well as a scaling behavior in the probability density of having traveled a certain distance at a certain time. Our results agree very well with the empirical observations reported in [D. Brockmann et al., Nature 439, 462 (2006)].

physics.data-an↗

arXiv2009

Role of Weak Ties in Link Prediction of Complex Networks

Plenty of algorithms for link prediction have been proposed and were applied to various real networks. Among these works, the weights of links are rarely taken into account. In this paper, we use local similarity indices to estimate the likelihood of the existence of links in weighted networks, including Common Neighbor, Adamic-Adar Index, Resource Allocation Index, and their weighted versions. In both the unweighted and weighted cases, the resource allocation index performs the best. To our surprise, the weighted indices perform worse, which reminds us of the well-known Weak Tie Theory. Further extensive experimental study shows that the weak ties play a significant role in the link prediction problem, and to emphasize the contribution of weak ties can remarkably enhance the predicting accuracy.

cs.IR↗

arXiv2009

Optimal transport on supply-demand networks

Previously, transport networks are usually treated as homogeneous networks, that is, every node has the same function, simultaneously providing and requiring resources. However, some real networks, such as power grid and supply chain networks, show a far different scenario in which the nodes are classified into two categories: the supply nodes provide some kinds of services, while the demand nodes require them. In this paper, we propose a general transport model for those supply-demand networks, associated with a criterion to quantify their transport capacities. In a supply-demand network with heterogenous degree distribution, its transport capacity strongly depends on the locations of supply nodes. We therefore design a simulated annealing algorithm to find the optimal configuration of supply nodes, which remarkably enhances the transport capacity, and outperforms the degree target algorithm, the betweenness target algorithm, and the greedy method. This work provides a start point for systematically analyzing and optimizing transport dynamics on supply-demand networks.

physics.data-an↗