64 research outputs found
Network Capacity Bound for Personalized PageRank in Multimodal Networks
In a former paper the concept of Bipartite PageRank was introduced and a
theorem on the limit of authority flowing between nodes for personalized
PageRank has been generalized. In this paper we want to extend those results to
multimodal networks. In particular we introduce a hypergraph type that may be
used for describing multimodal network where a hyperlink connects nodes from
each of the modalities. We introduce a generalisation of PageRank for such
graphs and define the respective random walk model that can be used for
computations. we finally state and prove theorems on the limit of outflow of
authority for cases where individual modalities have identical and distinct
damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373
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
HypeRS: Building a Hypergraph-driven ensemble Recommender System
Recommender systems are designed to predict user preferences over collections
of items. These systems process users' previous interactions to decide which
items should be ranked higher to satisfy their desires. An ensemble recommender
system can achieve great recommendation performance by effectively combining
the decisions generated by individual models. In this paper, we propose a novel
ensemble recommender system that combines predictions made by different models
into a unified hypergraph ranking framework. This is the first time that
hypergraph ranking has been employed to model an ensemble of recommender
systems. Hypergraphs are generalizations of graphs where multiple vertices can
be connected via hyperedges, efficiently modeling high-order relations. We
differentiate real and predicted connections between users and items by
assigning different hyperedge weights to individual recommender systems. We
perform experiments using four datasets from the fields of movie, music and
news media recommendation. The obtained results show that the ensemble
hypergraph ranking method generates more accurate recommendations compared to
the individual models and a weighted hybrid approach. The assignment of
different hyperedge weights to the ensemble hypergraph further improves the
performance compared to a setting with identical hyperedge weights
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Comparing the hierarchy of keywords in on-line news portals
The tagging of on-line content with informative keywords is a widespread
phenomenon from scientific article repositories through blogs to on-line news
portals. In most of the cases, the tags on a given item are free words chosen
by the authors independently. Therefore, relations among keywords in a
collection of news items is unknown. However, in most cases the topics and
concepts described by these keywords are forming a latent hierarchy, with the
more general topics and categories at the top, and more specialised ones at the
bottom. Here we apply a recent, cooccurrence-based tag hierarchy extraction
method to sets of keywords obtained from four different on-line news portals.
The resulting hierarchies show substantial differences not just in the topics
rendered as important (being at the top of the hierarchy) or of less interest
(categorised low in the hierarchy), but also in the underlying network
structure. This reveals discrepancies between the plausible keyword association
frameworks in the studied news portals
Uncovering the structure of hypergraphs through tensor decomposition: an application to folksonomy analysis
Folksonomies - shared vocabularies generated by users through collective annotation (tagging) of web-based content, which are formally hypergraphs connecting users, tags and objects, are beginning to play an increasingly important role in social media. Effective use of folksonomies for organizing and locating web content, discovering and organizing user communities in order to facilitate the contact and collaboration between users who share parts of their interests and attitudes calls for effective methods for discovering coherent groupings of users, objects, and tags. We empirically compare the results of several folksonomy clustering methods using tensor decompositions such as PARAFAC, Tucker3 and HOSVD which are generalizations of principal component analysis and singular value decomposition with standard methods that use 2-dimensional projections of the original 3-way relationships. Our results suggest that the proposed methods overcome some of the limitations of 2-way decomposition methods in clustering folksonomies
Multiobjective e-commerce recommendations based on hypergraph ranking
© 2018 Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
Personalized emotion recognition by personality-aware high-order learning of physiological signals
Due to the subjective responses of different subjects to physical stimuli, emotion recognition methodologies from physiological signals are increasingly becoming personalized. Existing works mainly focused on modeling the involved physiological corpus of each subject, without considering the psychological factors, such as interest and personality. The latent correlation among different subjects has also been rarely examined. In this article, we propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hyper-graphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we learn the weights for each of them. As the hypergraphs connect different subjects on the compound vertices, the emotions of multiple subjects can be simultaneously recognized. In this way, the constructed hypergraphs are vertex-weighted multi-modal multi-task ones. The estimated factors, referred to as emotion relevance, are employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art emotion recognition approaches
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