51,834 research outputs found
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
In a variety of application domains the content to be recommended to users is
associated with text. This includes research papers, movies with associated
plot summaries, news articles, blog posts, etc. Recommendation approaches based
on latent factor models can be extended naturally to leverage text by employing
an explicit mapping from text to factors. This enables recommendations for new,
unseen content, and may generalize better, since the factors for all items are
produced by a compactly-parametrized model. Previous work has used topic models
or averages of word embeddings for this mapping. In this paper we present a
method leveraging deep recurrent neural networks to encode the text sequence
into a latent vector, specifically gated recurrent units (GRUs) trained
end-to-end on the collaborative filtering task. For the task of scientific
paper recommendation, this yields models with significantly higher accuracy. In
cold-start scenarios, we beat the previous state-of-the-art, all of which
ignore word order. Performance is further improved by multi-task learning,
where the text encoder network is trained for a combination of content
recommendation and item metadata prediction. This regularizes the collaborative
filtering model, ameliorating the problem of sparsity of the observed rating
matrix.Comment: 8 page
Toward a collective intelligence recommender system for education
The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning?
This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students.
The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool.
Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out:
• It improves student motivation, as it helps you discover new content of interest in an easy way.
• It saves time in the search and classification of teaching material of interest.
• It fosters specialized reading, inspires competence as a means of learning.
• It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material.
The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft
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
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection
Detecting whether a news article is fake or genuine is a crucial task in
today's digital world where it's easy to create and spread a misleading news
article. This is especially true of news stories shared on social media since
they don't undergo any stringent journalistic checking associated with main
stream media. Given the inherent human tendency to share information with their
social connections at a mouse-click, fake news articles masquerading as real
ones, tend to spread widely and virally. The presence of echo chambers (people
sharing same beliefs) in social networks, only adds to this problem of
wide-spread existence of fake news on social media. In this paper, we tackle
the problem of fake news detection from social media by exploiting the very
presence of echo chambers that exist within the social network of users to
obtain an efficient and informative latent representation of the news article.
By modeling the echo-chambers as closely-connected communities within the
social network, we represent a news article as a 3-mode tensor of the structure
- and propose a tensor factorization based method to
encode the news article in a latent embedding space preserving the community
structure. We also propose an extension of the above method, which jointly
models the community and content information of the news article through a
coupled matrix-tensor factorization framework. We empirically demonstrate the
efficacy of our method for the task of Fake News Detection over two real-world
datasets. Further, we validate the generalization of the resulting embeddings
over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and
\textbf{2)} Collaborative News Recommendation. Our proposed method outperforms
appropriate baselines for both the tasks, establishing its generalization.Comment: Presented at ASONAM'1
- …