152,362 research outputs found
Effective Mechanism for Social Recommendation of News
Recommendation systems represent an important tool for news distribution on
the Internet. In this work we modify a recently proposed social recommendation
model in order to deal with no explicit ratings of users on news. The model
consists of a network of users which continually adapts in order to achieve an
efficient news traffic. To optimize network's topology we propose different
stochastic algorithms that are scalable with respect to the network's size.
Agent-based simulations reveal the features and the performance of these
algorithms. To overcome the resultant drawbacks of each method we introduce two
improved algorithms and show that they can optimize network's topology almost
as fast and effectively as other not-scalable methods that make use of much
more information
Emergence of scale-free leadership structure in social recommender systems
The study of the organization of social networks is important for
understanding of opinion formation, rumor spreading, and the emergence of
trends and fashion. This paper reports empirical analysis of networks extracted
from four leading sites with social functionality (Delicious, Flickr, Twitter
and YouTube) and shows that they all display a scale-free leadership structure.
To reproduce this feature, we propose an adaptive network model driven by
social recommending. Artificial agent-based simulations of this model highlight
a "good get richer" mechanism where users with broad interests and good
judgments are likely to become popular leaders for the others. Simulations also
indicate that the studied social recommendation mechanism can gradually improve
the user experience by adapting to tastes of its users. Finally we outline
implications for real online resource-sharing systems
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles
In micro-blogging platforms, people connect and interact with others.
However, due to cognitive biases, they tend to interact with like-minded people
and read agreeable information only. Many efforts to make people connect with
those who think differently have not worked well. In this paper, we
hypothesize, first, that previous approaches have not worked because they have
been direct -- they have tried to explicitly connect people with those having
opposing views on sensitive issues. Second, that neither recommendation or
presentation of information by themselves are enough to encourage behavioral
change. We propose a platform that mixes a recommender algorithm and a
visualization-based user interface to explore recommendations. It recommends
politically diverse profiles in terms of distance of latent topics, and
displays those recommendations in a visual representation of each user's
personal content. We performed an "in the wild" evaluation of this platform,
and found that people explored more recommendations when using a biased
algorithm instead of ours. In line with our hypothesis, we also found that the
mixture of our recommender algorithm and our user interface, allowed
politically interested users to exhibit an unbiased exploration of the
recommended profiles. Finally, our results contribute insights in two aspects:
first, which individual differences are important when designing platforms
aimed at behavioral change; and second, which algorithms and user interfaces
should be mixed to help users avoid cognitive mechanisms that lead to biased
behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User
Interfaces 201
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