2,786 research outputs found
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
Design and evaluation of improvement method on the Web information navigation - a stochastic search approach
With the advent of fast growing Internet and World Wide Web (WWW), more and more companies start the electronic commerce to enhance the business competitiveness. On the other hand, more and more people surf on the Web for information gathering/processing. Due to unbalanced traffic and poorly organized information, users suffer the slow communication and disordered information organization. The information provider can analyze the traffic and uniform resource locator (URL) counters to adjust the organization; however, heterogeneous navigation patterns and dynamic fluctuating Web traffic make the tuning process very complicated. Alternatively the user may be provided with guidance to navigate through the Web pages efficiently. In this paper, a Web site was modeled as a Markov chain associated with the corresponding dynamic traffic and designated information pages. We consider four models: inexperienced surfers on guidance-less sites, experienced surfers on guidance-less sites, sites with the mean-length guidance, and sites with the known-first-arc guidance (generalized as sites with dynamic stochastic shortest path guidance). Simulation is conducted to evaluate the performance of the different types of navigation guidance. We also propose a reformulation policy to highlight the hyperlinks as steering guidance. The evolution on complexity and applicability is also discussed for the design guideline of general improvement methods. The paper concludes with the summary and future directions.published_or_final_versio
LFGCN: Levitating over Graphs with Levy Flights
Due to high utility in many applications, from social networks to blockchain
to power grids, deep learning on non-Euclidean objects such as graphs and
manifolds, coined Geometric Deep Learning (GDL), continues to gain an ever
increasing interest. We propose a new L\'evy Flights Graph Convolutional
Networks (LFGCN) method for semi-supervised learning, which casts the L\'evy
Flights into random walks on graphs and, as a result, allows both to accurately
account for the intrinsic graph topology and to substantially improve
classification performance, especially for heterogeneous graphs. Furthermore,
we propose a new preferential P-DropEdge method based on the Girvan-Newman
argument. That is, in contrast to uniform removing of edges as in DropEdge,
following the Girvan-Newman algorithm, we detect network periphery structures
using information on edge betweenness and then remove edges according to their
betweenness centrality. Our experimental results on semi-supervised node
classification tasks demonstrate that the LFGCN coupled with P-DropEdge
accelerates the training task, increases stability and further improves
predictive accuracy of learned graph topology structure. Finally, in our case
studies we bring the machinery of LFGCN and other deep networks tools to
analysis of power grid networks - the area where the utility of GDL remains
untapped.Comment: To Appear in the 2020 IEEE International Conference on Data Mining
(ICDM
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Heterogeneous Information Networks (HINs) are information networks with
multiple types of nodes and edges. The concept of meta-path, i.e., a sequence
of entity types and relation types connecting two entities, is proposed to
provide the meta-level explainable semantics for various HIN tasks.
Traditionally, meta-paths are primarily used for schema-simple HINs, e.g.,
bibliographic networks with only a few entity types, where meta-paths are often
enumerated with domain knowledge. However, the adoption of meta-paths for
schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and
relation types, has been limited due to the computational complexity associated
with meta-path enumeration. Additionally, effectively assessing meta-paths
requires enumerating relevant path instances, which adds further complexity to
the meta-path learning process. To address these challenges, we propose
SchemaWalk, an inductive meta-path learning framework for schema-complex HINs.
We represent meta-paths with schema-level representations to support the
learning of the scores of meta-paths for varying relations, mitigating the need
of exhaustive path instance enumeration for each relation. Further, we design a
reinforcement-learning based path-finding agent, which directly navigates the
network schema (i.e., schema graph) to learn policies for establishing
meta-paths with high coverage and confidence for multiple relations. Extensive
experiments on real data sets demonstrate the effectiveness of our proposed
paradigm
Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data
Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM
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