7,552 research outputs found
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
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
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
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