1,846 research outputs found
Dynamics, robustness and fragility of trust
Trust is often conveyed through delegation, or through recommendation. This
makes the trust authorities, who process and publish trust recommendations,
into an attractive target for attacks and spoofing. In some recent empiric
studies, this was shown to lead to a remarkable phenomenon of *adverse
selection*: a greater percentage of unreliable or malicious web merchants were
found among those with certain types of trust certificates, then among those
without. While such findings can be attributed to a lack of diligence in trust
authorities, or even to conflicts of interest, our analysis of trust dynamics
suggests that public trust networks would probably remain vulnerable even if
trust authorities were perfectly diligent. The reason is that the process of
trust building, if trust is not breached too often, naturally leads to
power-law distributions: the rich get richer, the trusted attract more trust.
The evolutionary processes with such distributions, ubiquitous in nature, are
known to be robust with respect to random failures, but vulnerable to adaptive
attacks. We recommend some ways to decrease the vulnerability of trust
building, and suggest some ideas for exploration.Comment: 17 pages; simplified the statement and the proof of the main theorem;
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The influence of national culture on the attitude towards mobile recommender systems
This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation
of Korea Grant funded by the Korean governmen
Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
Collaborative filtering algorithms haven been widely used in recommender
systems. However, they often suffer from the data sparsity and cold start
problems. With the increasing popularity of social media, these problems may be
solved by using social-based recommendation. Social-based recommendation, as an
emerging research area, uses social information to help mitigate the data
sparsity and cold start problems, and it has been demonstrated that the
social-based recommendation algorithms can efficiently improve the
recommendation performance. However, few of the existing algorithms have
considered using multiple types of relations within one social network. In this
paper, we investigate the social-based recommendation algorithms on
heterogeneous social networks and proposed Hete-CF, a Social Collaborative
Filtering algorithm using heterogeneous relations. Distinct from the exiting
methods, Hete-CF can effectively utilize multiple types of relations in a
heterogeneous social network. In addition, Hete-CF is a general approach and
can be used in arbitrary social networks, including event based social
networks, location based social networks, and any other types of heterogeneous
information networks associated with social information. The experimental
results on two real-world data sets, DBLP (a typical heterogeneous information
network) and Meetup (a typical event based social network) show the
effectiveness and efficiency of our algorithm
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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