32,115 research outputs found
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
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
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Markets, herding and response to external information
We focus on the influence of external sources of information upon financial
markets. In particular, we develop a stochastic agent-based market model
characterized by a certain herding behavior as well as allowing traders to be
influenced by an external dynamic signal of information. This signal can be
interpreted as a time-varying advertising, public perception or rumor, in favor
or against one of two possible trading behaviors, thus breaking the symmetry of
the system and acting as a continuously varying exogenous shock. As an
illustration, we use a well-known German Indicator of Economic Sentiment as
information input and compare our results with Germany's leading stock market
index, the DAX, in order to calibrate some of the model parameters. We study
the conditions for the ensemble of agents to more accurately follow the
information input signal. The response of the system to the external
information is maximal for an intermediate range of values of a market
parameter, suggesting the existence of three different market regimes:
amplification, precise assimilation and undervaluation of incoming information.Comment: 30 pages, 8 figures. Thoroughly revised and updated version of
arXiv:1302.647
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