184,987 research outputs found
Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective
Understanding mechanisms driving link formation in dynamic social networks is
a long-standing problem that has implications to understanding social structure
as well as link prediction and recommendation. Social networks exhibit a high
degree of transitivity, which explains the successes of common neighbor-based
methods for link prediction. In this paper, we examine mechanisms behind link
formation from the perspective of an ego node. We introduce the notion of
personalized degree for each neighbor node of the ego, which is the number of
other neighbors a particular neighbor is connected to. From empirical analyses
on four on-line social network datasets, we find that neighbors with higher
personalized degree are more likely to lead to new link formations when they
serve as common neighbors with other nodes, both in undirected and directed
settings. This is complementary to the finding of Adamic and Adar that neighbor
nodes with higher (global) degree are less likely to lead to new link
formations. Furthermore, on directed networks, we find that personalized
out-degree has a stronger effect on link formation than personalized in-degree,
whereas global in-degree has a stronger effect than global out-degree. We
validate our empirical findings through several link recommendation experiments
and observe that incorporating both personalized and global degree into link
recommendation greatly improves accuracy.Comment: To appear at the 10th International Workshop on Modeling Social Media
co-located with the Web Conference 201
Reciprocal Recommendation System for Online Dating
Online dating sites have become popular platforms for people to look for
potential romantic partners. Different from traditional user-item
recommendations where the goal is to match items (e.g., books, videos, etc)
with a user's interests, a recommendation system for online dating aims to
match people who are mutually interested in and likely to communicate with each
other. We introduce similarity measures that capture the unique features and
characteristics of the online dating network, for example, the interest
similarity between two users if they send messages to same users, and
attractiveness similarity if they receive messages from same users. A
reciprocal score that measures the compatibility between a user and each
potential dating candidate is computed and the recommendation list is generated
to include users with top scores. The performance of our proposed
recommendation system is evaluated on a real-world dataset from a major online
dating site in China. The results show that our recommendation algorithms
significantly outperform previously proposed approaches, and the collaborative
filtering-based algorithms achieve much better performance than content-based
algorithms in both precision and recall. Our results also reveal interesting
behavioral difference between male and female users when it comes to looking
for potential dates. In particular, males tend to be focused on their own
interest and oblivious towards their attractiveness to potential dates, while
females are more conscientious to their own attractiveness to the other side of
the line
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
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