24,804 research outputs found
Social Recommendation Systems
In recent years, with the rise of online social networks, personalized recommendations that leverage the aspect of social connections have become a very intriguing domain for researchers. In this work, we explore how influence propagation and the decay in the cascading effect of influence from influential users can be leveraged to generate social graph-based recommendations. Understanding how influence propagates within a social network is itself a challenging problem. In this research, we model the decay in influence propagation in directed graphs, utilizing the structural properties of the social graph to measure the propagated influence beyond one-hop. This social network information from influence propagation is also combined with matrix factorization as a social regularization factor. We then employ this unified framework to form social recommendations, and present our experimental results using real-life datasets. Extensive experimental analysis demonstrate that our proposed methodology outperforms state-of-the-art techniques for generating social recommendations
Musical recommendations and personalization in a social network
This paper presents a set of algorithms used for music recommendations and
personalization in a general purpose social network www.ok.ru, the second
largest social network in the CIS visited by more then 40 millions users per
day. In addition to classical recommendation features like "recommend a
sequence" and "find similar items" the paper describes novel algorithms for
construction of context aware recommendations, personalization of the service,
handling of the cold-start problem, and more. All algorithms described in the
paper are working on-line and are able to detect and address changes in the
user's behavior and needs in the real time.
The core component of the algorithms is a taste graph containing information
about different entities (users, tracks, artists, etc.) and relations between
them (for example, user A likes song B with certainty X, track B created by
artist C, artist C is similar to artist D with certainty Y and so on). Using
the graph it is possible to select tracks a user would most probably like, to
arrange them in a way that they match each other well, to estimate which items
from a fixed list are most relevant for the user, and more.
In addition, the paper describes the approach used to estimate algorithms
efficiency and analyze the impact of different recommendation related features
on the users' behavior and overall activity at the service.Comment: This is a full version of a 4 pages article published at ACM RecSys
201
Affinity Paths and Information Diffusion in Social Networks
Widespread interest in the diffusion of information through social networks
has produced a large number of Social Dynamics models. A majority of them use
theoretical hypothesis to explain their diffusion mechanisms while the few
empirically based ones average out their measures over many messages of
different content. Our empirical research tracking the step-by-step email
propagation of an invariable viral marketing message delves into the content
impact and has discovered new and striking features. The topology and dynamics
of the propagation cascades display patterns not inherited from the email
networks carrying the message. Their disconnected, low transitivity, tree-like
cascades present positive correlation between their nodes probability to
forward the message and the average number of neighbors they target and show
increased participants' involvement as the propagation paths length grows. Such
patterns not described before, nor replicated by any of the existing models of
information diffusion, can be explained if participants make their pass-along
decisions based uniquely on local knowledge of their network neighbors affinity
with the message content. We prove the plausibility of such mechanism through a
stylized, agent-based model that replicates the \emph{Affinity Paths} observed
in real information diffusion cascades.Comment: 11 pages, 7 figure
Detection of Trending Topic Communities: Bridging Content Creators and Distributors
The rise of a trending topic on Twitter or Facebook leads to the temporal
emergence of a set of users currently interested in that topic. Given the
temporary nature of the links between these users, being able to dynamically
identify communities of users related to this trending topic would allow for a
rapid spread of information. Indeed, individual users inside a community might
receive recommendations of content generated by the other users, or the
community as a whole could receive group recommendations, with new content
related to that trending topic. In this paper, we tackle this challenge, by
identifying coherent topic-dependent user groups, linking those who generate
the content (creators) and those who spread this content, e.g., by
retweeting/reposting it (distributors). This is a novel problem on
group-to-group interactions in the context of recommender systems. Analysis on
real-world Twitter data compare our proposal with a baseline approach that
considers the retweeting activity, and validate it with standard metrics.
Results show the effectiveness of our approach to identify communities
interested in a topic where each includes content creators and content
distributors, facilitating users' interactions and the spread of new
information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc
The Limits of Popularity-Based Recommendations, and the Role of Social Ties
In this paper we introduce a mathematical model that captures some of the
salient features of recommender systems that are based on popularity and that
try to exploit social ties among the users. We show that, under very general
conditions, the market always converges to a steady state, for which we are
able to give an explicit form. Thanks to this we can tell rather precisely how
much a market is altered by a recommendation system, and determine the power of
users to influence others. Our theoretical results are complemented by
experiments with real world social networks showing that social graphs prevent
large market distortions in spite of the presence of highly influential users.Comment: 10 pages, 9 figures, KDD 201
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