20,533 research outputs found
The Wisdom of Networks: Matching Recommender Systems and Social Network Theories
This paper aims to analyzing the match between social network theories and recommender systems. Several social network theories provide explanations on why nodes link to each others. At the same time, recommender systems recommend users to connect to some items according to different internal algorithms. The study identifies the theoretical mechanisms behind the main types of recommender algorithms, and specifically behind network-based ones. Main design implications for recommender algorithms are derived
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Network-Based Models for Social Recommender Systems
With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modelling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets
Network-based models for social recommender systems
With the overwhelming online products available in recent years, there is an
increasing need to filter and deliver relevant personalized advice for users.
Recommender systems solve this problem by modeling and predicting individual
preferences for a great variety of items such as movies, books or research
articles. In this chapter, we explore rigorous network-based models that
outperform leading approaches for recommendation. The network models we
consider are based on the explicit assumption that there are groups of
individuals and of items, and that the preferences of an individual for an item
are determined only by their group memberships. The accurate prediction of
individual user preferences over items can be accomplished by different
methodologies, such as Monte Carlo sampling or Expectation-Maximization
methods, the latter resulting in a scalable algorithm which is suitable for
large datasets
Adaptive model for recommendation of news
Most news recommender systems try to identify users' interests and news'
attributes and use them to obtain recommendations. Here we propose an adaptive
model which combines similarities in users' rating patterns with epidemic-like
spreading of news on an evolving network. We study the model by computer
agent-based simulations, measure its performance and discuss its robustness
against bias and malicious behavior. Subject to the approval fraction of news
recommended, the proposed model outperforms the widely adopted recommendation
of news according to their absolute or relative popularity. This model provides
a general social mechanism for recommender systems and may find its
applications also in other types of recommendation.Comment: 6 pages, 6 figure
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