An improved recommendation algorithm via weakening indirect linkage effect

We propose an indirect-link-weakened mass diffusion method(IMD), by considering the indirect linkage and the source object heterogeneity effect in the mass diffusion(MD) recommendation method. Experimental results on the MovieLens, Netflix, and RYM datasets show that, the IMD method greatly improves both the recommendation accuracy and diversity, compared with a heterogeneity-weakened MD method(HMD), which only considers the source object heterogeneity. Moreover, the recommendation accuracy of the cold objects is also better elevated in the IMD than the HMD method. It suggests that eliminating the redundancy induced by the indirect linkages could have a prominent effect on the recommendation efficiency in the MD method.     

Temporal inequalities for sequential multi-time actions in quantum information processing

A new kind of temporal inequalities are discussed, which apply to algorithmic processes, involving a finite memory processing unit. They are an alternative to the Leggett-Grag ones, as well as to the modified ones by Brukner et al. If one considers comparison of quantum anti classical processes involving systems of finite memory （of the same capacity in both cases）, the inequalities give a clear message why we can expect quantum speed-up. In a classical process one always has clearly defined values of possible measurements, or in terms of the information processing language, if we have a sequential computations of some function depending on data arriving at each step on an algorithm, the function always has a clearly defined value. In the quantum case only the final value, after the end of the algorithm, is defined. All intermediate values, in agreement with Bohr＇s complementarity, cannot be ascribed a definite value.     

The effect of TTL on the information traffic

In real communication protocols, the information packets have a finite Time-to-Live (TTL) to avoid the waste of network resources, such as infinite loop induced by routing error or too long transferring time. In this paper, we introduce TTL into the information traffic model on Barabási-Albert scale-free networks under local routing strategy and focus on its effect on the network capacity measured by the critical point (R_c) of phase transition from free flow to congestion. Simulations show that the network capacity and the communication velocity are improved. However, some packets are dropped before they arrived at destinations. It is found that the share of successfully arrived packets monotonously increases with the increment of TTL and it is considerably acceptable if TTL is not very small. We also examine the effect of TTL on the positive-feedback preference (PFP) internet model and the results are alike. Our work may be helpful in quantifying the effect of packet lifetime in real communication networks and in routing strategy designing.     

Temporal Talbot effect applied to determine dispersion parameters

The space–time duality theory and the temporal selfimaging phenomenon (or Talbot effect) are used to propose a method for determining dispersion parameters associated with an optic fiber link. From the space–time analogy, the actions of free-space propagation and phase curvatures taking place in the general spatial Talbot effect are implemented for time-varying wavefields. Using the temporal selfimaging conditions, a relationship is derived for determining the second-order dispersion coefficient of a given dispersive medium under test. As a particular application, we analyze the feasibility of the measuring approach using a linearly chirped fiber grating as the dispersive component under test. Some simulations are carried out in order to study the sensitivity and accuracy of the developed method.     

Differentiating information transfer and causal effect

The concepts of information transfer and causal effect have received much recent attention, yet often the two are not appropriately distinguished and certain measures have been suggested to be suitable for both. We discuss two existing measures, transfer entropy and information flow, which can be used separately to quantify information transfer and causal information flow respectively. We apply these measures to cellular automata on a local scale in space and time, in order to explicitly contrast them and emphasize the differences between information transfer and causality. We also describe the manner in which the measures are complementary, including the conditions under which they in fact converge. We show that causal information flow is a primary tool to describe the causal structure of a system, while information transfer can then be used to describe the emergent computation on that causal structure.     

Behavior patterns of online users and the effect on information filtering

Understanding the structure and evolution of web-based user-item bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users’ behavior and the effect on recommendation process. Empirical analysis on the e-commercial systems show that users’ taste preferences are heterogeneous in general but their interests for niche items are highly clustered. Additionally, recommendation processes are investigated on both the real networks and the reshuffled networks in which real users’ behavior patterns can be gradually destroyed. We find that the performance of personalized recommendation methods is strongly related to the real network structure. Detailed study on each item shows that most hot items are accurately recommended and their recommendation accuracy is robust to the reshuffling process. However, the accuracy for niche items is relatively low and drops significantly after removing users’ behavior patterns. Our work is also meaningful in practical sense since it reveals an effective direction to improve the accuracy and the robustness of the existing recommender systems.     

Personal recommendation via unequal resource allocation on bipartite networks

In this paper, we present a recommendation algorithm based on the resource-allocation progresses on bipartite networks. In this model, each node is assigned an attraction that is proportional to the power of its degree, where the exponent β is an adjustable parameter that controls the configuration of attractions. In the resource-allocation process, each transmitter distributes its each neighbor a fragment of resource that is proportional to the attraction of the neighbor. Based on a benchmark database, we find that decreasing the attractions that the nodes with higher degrees are assigned can further improve the algorithmic accuracy. More significantly, numerical results show that the optimal configuration of attractions subject to accuracy can also generate more diverse and less popular recommendations.     

Temporal solitary waves due to optical rectification and the electro-optic effect

Temporal solitary waves that are due to the mutual interaction of optical rectification and the electro-optic effect in the presence of a second-order nonlinearity are studied. It is found that a two-parameter family of solitons exists. Analytical solutions are found for special cases. Numerical soliton solutions of the system of equations include single- and multiple-hump solitary waves.     

The effect of atomic motion and two-quanta JCM on the information entropy

We study the interaction between a moving two-level atom and a single-mode field. The coupled atom-cavity system with atomic center-of-mass motion included is modeled by considering the dependence of the atomic motion along z-axis. At exact resonance between the internal atomic transition and the cavity eigenfrequency, an exact solution of the system is obtained and periodically modulated Rabi oscillations and regular translational motion are observed. We focused on the dynamics of both field Wehrl entropy and Wehrl phase distribution. The influence of the atomic motion on the evolution of von Neumann entropy and Wehrl entropy is examined. The results show that the atomic motion and the field-mode structure play important roles in the evolution of the von Neumann entropy, Wehrl entropy and Wehrl PD.     

Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs

Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user-item-tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.     

Topology of music recommendation networks

We study the topology of several music recommendation networks, which arise from relationships between artist, co-occurrence of songs in play lists or experts' recommendation. The analysis uncovers the emergence of complex network phenomena in these kinds of recommendation networks, built considering artists as nodes and their resemblance as links. We observe structural properties that provide some hints on navigation and possible optimizations on the design of music recommendation systems. Finally, the analysis derived from existing music knowledge sources provides a deeper understanding of the human music similarity perception.     

Fisher information: uncertainty relation and steric effect

The Fisher information has a locality property which allows one to quantify the gradient content of the quantum-mechanical states of a physical system, so grasping the distinctive oscillatory character of the corresponding wavefunctions. We discuss the uncertainty relation fulfilled by the position and momentum Fisher information for general systems, and the tighter uncertainty relation satisfied by the eigenfunctions of arbitrary central potentials. Moreover, we show the usefulness of these two quantities together with the measure of the steric effect for the explanation of the origin of the internal rotation barrier between the eclipsed and staggered conformers of ethane.     

Effects of high-order correlations on personalized recommendations for bipartite networks

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user-user correlations are obtained by a diffusion process. Furthermore, by considering the second-order correlations, we design an effective algorithm that depresses the influence of mainstream preferences. Simulation results show that the algorithmic accuracy, measured by the average ranking score, is further improved by 20.45% and 33.25% in the optimal cases of MovieLens and Netflix data. More importantly, the optimal value depends approximately monotonously on the sparsity of the training set. Given a real system, we could estimate the optimal parameter according to the data sparsity, which makes this algorithm easy to be applied. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that as the sparsity increases, the algorithm considering the second-order correlation can outperform the MCF simultaneously in all three criteria.     

Heat conduction process on community networks as a recommendation model

Using heat conduction mechanism on a social network we develop a systematic method to predict missing values as recommendations. This method can treat very large matrices that are typical of internet communities. In particular, with an innovative, exact formulation that accommodates arbitrary boundary condition, our method is easy to use in real applications. The performance is assessed by comparing with traditional recommendation methods using real data.     

Temporal analysis of low-amplitude screening spatial solitons due to two-photon photorefractive effect

This paper investigates the temporal behavior of two-photon screening spatial solitons using a numerical technique. An expression for the time-dependent dynamical evolution is obtained. The results show that the width of bright solitons decreases monotonically to a minimum toward steady state value when the intensity ratio of the solitons is small, which is in low-amplitude regime. Within the same propagation time, the intensity width of solitons decreases with increase in soliton intensity ratio and with increase in external electric field. The formation time of solitons is independent of the intensity ratio of the solitons or the external electric field and is close to a constant determined by the dielectric relaxation time. The temporal properties of dark solitons are similar to those of the bright solitons.     

The effect of smoothing filter slope and spectral frequency on temporal speech information

It is known that information contained within the filter skirts can provide cues important to speech intelligibility. However, the role of filter slope during temporal smoothing has received little attention. In experiment 1, smoothing filter slope angle was found to have a large effect on the intelligibility of sentences represented by three amplitude-modulated sinusoids. In experiment 2, the use of temporal cues above 16 Hz was examined across various regions of the spectrum. When increases in rate were presented to individual spectral bands, intelligibility only increased when presented in the higher spectral region. This result suggests a greater reliance on higher-rate cues in this region. However, intelligibility was greatest when these cues were distributed across the spectrum, indicating that their effective use is not restricted solely to this region.     

Personal recommendation via modified collaborative filtering

In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard cosine similarity, we take into account the influence of a node’s degree. Substituting this new definition of similarity for the standard cosine similarity, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF.     

基于视频推荐技术的无线网络视频服务器设计

无线网络视频服务器中视频推荐技术已成为重要技术之一,视频推荐技术是为了用户在使用无线网络是视频务器时,更快的找到感兴趣的视频。采用当前方法对用户进行视频推荐时,未考虑用户的兴趣偏好是否随着时间有所变化,使视频推荐出现偏差。为此,提出一种基于视频推荐技术的无线网络视频服务器设计方法。该方法首先使用无线网络视频服务器硬件部分的MPEG-4进行视频数据采集,并对MPEG-4采集的视频数据进行整理。在由软件部分把采集到的视频数据储存到缓冲区,进行视频缓冲,并建立视频数据队列进行视频数据输送。以计算无线网络视频的相似度来搜索相近视频,将搜索到的视频与目标用户观看过的视频进行对比,对比相似度越接近1,则说明用户对推荐视频感兴趣的几率大,反之越接近-1用户对推荐视频越不感兴趣。此计算方法能有效的从海量视频数据中快速的搜索出目标用户感兴趣视频。实验结果表明,将视频推荐技术应用到无线网络视频中可以迅速准确的搜索出目标用户感兴趣视频。     

Preference of online users and personalized recommendations

In a recent work [T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Proc. Natl. Acad. Sci. 107 (2010) 4511], a personalized recommendation algorithm with high performance in both accuracy and diversity is proposed. This method is based on the hybridization of two single algorithms called probability spreading and heat conduction, which respectively are inclined to recommend popular and unpopular products. With a tunable parameter, an optimal balance between these two algorithms in system level is obtained. In this paper, we apply this hybrid method in individual level, namely each user has his/her own personalized hybrid parameter to adjust. Interestingly, we find that users are quite different in personalized hybrid parameters and the recommendation performance can be significantly improved if each user is assigned with his/her optimal personalized hybrid parameter. Furthermore, we find that users’ personalized parameters are negatively correlated with users’ degree but positively correlated with the average degree of the items collected by each user. With these understandings, we propose a strategy to assign users with suitable personalized parameters, which leads to a further improvement of the original hybrid method. Finally, our work highlights the importance of considering the heterogeneity of users in recommendation.