21 research outputs found
Avoiding congestion in recommender systems
Recommender systems use the historical activities and personal profiles of users to uncover their preferences and recommend objects. Most of the previous methods are based on objects' (and/or users') similarity rather than on their difference. Such approaches are subject to a high risk of increasingly exposing users to a narrowing band of popular objects. As a result, a few objects may be recommended to an enormous number of users, resulting in the problem of recommendation congestion, which is to be avoided, especially when the recommended objects are limited resources. In order to quantitatively measure a recommendation algorithmÊŒs ability to avoid congestion, we proposed a new metric inspired by the Gini index, which is used to measure the inequality of the individual wealth distribution in an economy. Besides this, a new recommendation method called directed weighted conduction (DWC) was developed by considering the heat conduction process on a userâobject bipartite network with different thermal conductivities. Experimental results obtained for three benchmark data sets showed that the DWC algorithm can effectively avoid system congestion, and greatly improve the novelty and diversity, while retaining relatively high accuracy, in comparison with the state-of-the-art methods
Unveiling Explosive Vulnerability of Networks through Edge Collective Behavior
Edges, binding together nodes within networks, have the potential to induce
dramatic transitions when specific collective failure behaviors emerge. These
changes, initially unfolding covertly and then erupting abruptly, pose
substantial, unforeseeable threats to networked systems, and are termed
explosive vulnerability. Thus, identifying influential edges capable of
triggering such drastic transitions, while minimizing cost, is of utmost
significance. Here, we address this challenge by introducing edge collective
influence (ECI), which builds upon the optimal percolation theory applied to
line graphs. ECI embodies features of both optimal and explosive percolation,
involving minimized removal costs and explosive dismantling tactic.
Furthermore, we introduce two improved versions of ECI, namely IECI and IECIR,
tailored for objectives of hidden and fast dismantling, respectively, with
their superior performance validated in both synthetic and empirical networks.
Finally, we present a dual competitive percolation (DCP) model, whose reverse
process replicates the explosive dismantling process and the trajectory of the
cost function of ECI, elucidating the microscopic mechanisms enabling ECI's
optimization. ECI and the DCP model demonstrate the profound connection between
optimal and explosive percolation. This work significantly deepens our
comprehension of percolation and provides valuable insights into the explosive
vulnerabilities arising from edge collective behaviors.Comment: 19 pages, 11 figures, 2 table
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Eliminating the Effect of Rating Bias on Reputation Systems
The ongoing rapid development of the e-commercial and interest-base websites makes it more pressing to evaluate objectsâ accurate quality before recommendation. The objectsâ quality is often calculated based on their historical information, such as selected records or rating scores. Usually high quality products obtain higher average ratings than low quality products regardless of rating biases or errors. However, many empirical cases demonstrate that consumers may be misled by rating scores added by unreliable users or deliberate tampering. In this case, usersâ reputation, that is, the ability to rate trustily and precisely, makes a big difference during the evaluation process. Thus, one of the main challenges in designing reputation systems is eliminating the effects of usersâ rating bias. To give an objective evaluation of each userâs reputation and uncover an objectâs intrinsic quality, we propose an iterative balance (IB) method to correct usersâ rating biases. Experiments on two datasets show that the IB method is a highly self-consistent and robust algorithm and it can accurately quantify moviesâ actual quality and usersâ stability of rating. Compared with existing methods, the IB method has higher ability to find the âdark horses,â that is, not so popular yet good movies, in the Academy Awards.ISSN:1076-2787ISSN:1099-052
Predicting the Popularity of Information on Social Platforms without Underlying Network Structure
The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, traditional methods either rely on complicated time-varying features that are challenging to extract from multilingual and cross-platform content, or on network structures and properties that are often difficult to obtain. To address these issues, we conducted empirical research using data from two well-known social networking platforms, WeChat and Weibo. Our findings suggest that the information-cascading process is best described as an activateâdecay dynamic process. Building on these insights, we developed an activateâdecay (AD)-based algorithm that can accurately predict the long-term popularity of online content based solely on its early repost amount. We tested our algorithm using data from WeChat and Weibo, demonstrating that we could fit the evolution trend of content propagation and predict the longer-term dynamics of message forwarding from earlier data. We also discovered a close correlation between the peak forwarding amount of information and the total amount of dissemination. Finding the peak of the amount of information dissemination can significantly improve the prediction accuracy of our model. Our method also outperformed existing baseline methods for predicting the popularity of information
Identifying vital nodes through augmented random walks on higher-order networks
Empirical networks possess considerable heterogeneity of node connections,
resulting in a small portion of nodes playing crucial roles in network
structure and function. Yet, how to characterize nodes' influence and identify
vital nodes is by far still unclear in the study of networks with higher-order
interactions. In this paper, we introduce a multi-order graph obtained by
incorporating the higher-order bipartite graph and the classical pairwise
graph, and propose a Higher-order Augmented Random Walk (HoRW) model through
random walking on it. This representation preserves as much information about
the higher-interacting network as possible. The results indicate that the
proposed method effectively addresses the localization problem of certain
classical centralities. In contrast to random walks along pairwise interactions
only, performing more walks along higher-order interactions assists in not only
identifying the most important nodes but also distinguishing nodes that ranked
in the middle and bottom. Our method outperforms classical centralities in
identifying vital nodes and can scale to various tasks in networks, including
information spread maximization and network dismantling problems. The proposed
higher-order representation and the random walk model provide novel insights
and potent tools for studying higher-order mechanisms and functionality