223 research outputs found
Heat Conduction Process on Community Networks as a Recommendation Model
Using heat conduction mechanism on a social network we develop a systematic
method to predict missing values as recommendations. This method can treat very
large matrices that are typical of internet communities. In particular, with an
innovative, exact formulation that accommodates arbitrary boundary condition,
our method is easy to use in real applications. The performance is assessed by
comparing with traditional recommendation methods using real data.Comment: 4 pages, 2 figure
Improving information filtering via network manipulation
Recommender system is a very promising way to address the problem of
overabundant information for online users. Though the information filtering for
the online commercial systems received much attention recently, almost all of
the previous works are dedicated to design new algorithms and consider the
user-item bipartite networks as given and constant information. However, many
problems for recommender systems such as the cold-start problem (i.e. low
recommendation accuracy for the small degree items) are actually due to the
limitation of the underlying user-item bipartite networks. In this letter, we
propose a strategy to enhance the performance of the already existing
recommendation algorithms by directly manipulating the user-item bipartite
networks, namely adding some virtual connections to the networks. Numerical
analyses on two benchmark data sets, MovieLens and Netflix, show that our
method can remarkably improve the recommendation performance. Specifically, it
not only improve the recommendations accuracy (especially for the small degree
items), but also help the recommender systems generate more diverse and novel
recommendations.Comment: 6 pages, 5 figure
How to project a bipartite network?
The one-mode projecting is extensively used to compress the bipartite
networks. Since the one-mode projection is always less informative than the
bipartite representation, a proper weighting method is required to better
retain the original information. In this article, inspired by the network-based
resource-allocation dynamics, we raise a weighting method, which can be
directly applied in extracting the hidden information of networks, with
remarkably better performance than the widely used global ranking method as
well as collaborative filtering. This work not only provides a creditable
method in compressing bipartite networks, but also highlights a possible way
for the better solution of a long-standing challenge in modern information
science: How to do personal recommendation?Comment: 7 pages, 4 figure
The reinforcing influence of recommendations on global diversification
Recommender systems are promising ways to filter the overabundant information
in modern society. Their algorithms help individuals to explore decent items,
but it is unclear how they allocate popularity among items. In this paper, we
simulate successive recommendations and measure their influence on the
dispersion of item popularity by Gini coefficient. Our result indicates that
local diffusion and collaborative filtering reinforce the popularity of hot
items, widening the popularity dispersion. On the other hand, the heat
conduction algorithm increases the popularity of the niche items and generates
smaller dispersion of item popularity. Simulations are compared to mean-field
predictions. Our results suggest that recommender systems have reinforcing
influence on global diversification.Comment: 6 pages, 6 figure
Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces
Recent research has unveiled the importance of online social networks for
improving the quality of recommender systems and encouraged the research
community to investigate better ways of exploiting the social information for
recommendations. To contribute to this sparse field of research, in this paper
we exploit users' interactions along three data sources (marketplace, social
network and location-based) to assess their performance in a barely studied
domain: recommending products and domains of interests (i.e., product
categories) to people in an online marketplace environment. To that end we
defined sets of content- and network-based user similarity features for each
data source and studied them isolated using an user-based Collaborative
Filtering (CF) approach and in combination via a hybrid recommender algorithm,
to assess which one provides the best recommendation performance.
Interestingly, in our experiments conducted on a rich dataset collected from
SecondLife, a popular online virtual world, we found that recommenders relying
on user similarity features obtained from the social network data clearly
yielded the best results in terms of accuracy in case of predicting products,
whereas the features obtained from the marketplace and location-based data
sources also obtained very good results in case of predicting categories. This
finding indicates that all three types of data sources are important and should
be taken into account depending on the level of specialization of the
recommendation task.Comment: 20 pages book chapte
YourMOOC4all: a recommender system for MOOCs based on collaborative filtering implementing UDL
YourMOOC4all is a pilot research project to collect feedback requests regarding accessible design for Massive Open Online Courses (MOOCs). In this online application, a specific website offers the possibility for any learner to freely judge if a particular MOOC complies Universal Design for Learning (UDL) principles. User feedback is of great value for the future development of MOOC platforms and MOOC educational resources, as it will help to follow De-sign for All guidelines. YourMOOC4all is a recommender system which gathers valuable information directly from learners to improve aspects such as the quality, accessibility and usability of this online learning environment. The final objective of collecting user’s feedback is to advice MOOC providers about the missing means for meeting learner needs. This paper describes the pedagogical and technological background of YourMOOC4all and its use cases
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Collaborative Filtering: A New Approach to Searching Digital Libraries
At Oregon State University (OSU), the School of Electrical Engineering and Computer Science (EECS) and the OSU Libraries are working together on a project to improve the effectiveness and accessibility of digital information created and collected by academic libraries. This project focuses on making digital resources more accessible through an innovative search interface that incorporates collaborative filtering. New approaches to search interfaces will help make the growing wealth of online content more accessible and useful. This paper discusses the problem, explains how collaborative filtering works, describes the System for Electronic Recommendation Filtering (SERF), and then presents initial results from an installation in the OSU Libraries. The productive collaboration at OSU between the Libraries and EECS portends the future of development of search systems; by working together, we can harness the expertise of librarians, computer scientists, and information users to develop more useful search interfaces and increase access to the libraries’ resources and services.Keywords: Digital libraries, Search systems, Collaborative filteringKeywords: Digital libraries, Search systems, Collaborative filterin
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SERF: integrating human recommendations with search
Today's university library has many digitally accessible resources, both indexes to content and considerable original content. Using off-the-shelf search technology provides a single point of access into library resources, but we have found that such full-text indexing technology is not entirely satisfactory for library searching.
In response to this, we report initial usage results from a prototype of an entirely new type of search engine - The System for Electronic Recommendation Filtering (SERF) - that we have designed and deployed for the Oregon State University (OSU) Libraries. SERF encourages users to enter longer and more informative queries, and collects ratings from users as to whether search results meet their information need or not. These ratings are used to make recommendations to later users with similar needs. Over time, SERF learns from the users what documents are valuable for what information needs.
In this paper, we focus on understanding whether such recommendations can increase other users' search efficiency and effectiveness in library website searching.
Based on examination of three months of usage as an alternative search interface available to all users of the Oregon State University Libraries website (http://osulibrary.oregonstate.edu/), we found strong evidence that the recommendations with human evaluation could increase the efficiency as well as effectiveness of the library website search process. Those users who received recommendations needed to examine fewer results, and recommended documents were rated much higher than documents returned by a traditional search engine
Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality
Matrix factorization (MF) is extensively used to mine the user preference
from explicit ratings in recommender systems. However, the reliability of
explicit ratings is not always consistent, because many factors may affect the
user's final evaluation on an item, including commercial advertising and a
friend's recommendation. Therefore, mining the reliable ratings of user is
critical to further improve the performance of the recommender system. In this
work, we analyze the deviation degree of each rating in overall rating
distribution of user and item, and propose the notion of user-based rating
centrality and item-based rating centrality, respectively. Moreover, based on
the rating centrality, we measure the reliability of each user rating and
provide an optimized matrix factorization recommendation algorithm.
Experimental results on two popular recommendation datasets reveal that our
method gets better performance compared with other matrix factorization
recommendation algorithms, especially on sparse datasets
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