2,480 research outputs found
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
Collaborative filtering (CF) has been successfully used to provide users with
personalized products and services. However, dealing with the increasing
sparseness of user-item matrix still remains a challenge. To tackle such issue,
hybrid CF such as combining with content based filtering and leveraging side
information of users and items has been extensively studied to enhance
performance. However, most of these approaches depend on hand-crafted feature
engineering, which are usually noise-prone and biased by different feature
extraction and selection schemes. In this paper, we propose a new hybrid model
by generalizing contractive auto-encoder paradigm into matrix factorization
framework with good scalability and computational efficiency, which jointly
model content information as representations of effectiveness and compactness,
and leverage implicit user feedback to make accurate recommendations. Extensive
experiments conducted over three large scale real datasets indicate the
proposed approach outperforms the compared methods for item recommendation.Comment: 4 pages, 3 figure
Kondo Metal and Ferrimagnetic Insulator on the Triangular Kagom\'e Lattice
We obtain the rich phase diagrams in the Hubbard model on the triangular
Kagom\'e lattice as a function of interaction, temperature and asymmetry, by
combining the cellular dynamical mean-field theory with the continuous time
quantum Monte Carlo method. The phase diagrams show the asymmetry separates the
critical points in Mott transition of two sublattices on the triangular
Kagom\'e lattice and produces two novel phases called plaquette insulator with
an obvious gap and a gapless Kondo metal. When the Coulomb interaction is
stronger than the critical value Uc, a short range paramagnetic insulating
phase, which is a candidate for the short rang resonating valence-bond spin
liquid, emerges before the ferrimagnetic order is formed independent of
asymmetry. Furthermore, we discuss how to measure these phases in future
experiments
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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