33,441 research outputs found
CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things
The Internet of Things (IoT) aims at interconnecting everyday objects
(including both things and users) and then using this connection information to
provide customized user services. However, IoT does not work in its initial
stages without adequate acquisition of user preferences. This is caused by
cold-start problem that is a situation where only few users are interconnected.
To this end, we propose CRUC scheme - Cold-start Recommendations Using
Collaborative Filtering in IoT, involving formulation, filtering and prediction
steps. Extensive experiments over real cases and simulation have been performed
to evaluate the performance of CRUC scheme. Experimental results show that CRUC
efficiently solves the cold-start problem in IoT.Comment: Elsevier ESEP 2011: 9-10 December 2011, Singapore, Elsevier Energy
Procedia, http://www.elsevier.com/locate/procedia/, 201
A Collective Variational Autoencoder for Top- Recommendation with Side Information
Recommender systems have been studied extensively due to their practical use
in many real-world scenarios. Despite this, generating effective
recommendations with sparse user ratings remains a challenge. Side information
associated with items has been widely utilized to address rating sparsity.
Existing recommendation models that use side information are linear and, hence,
have restricted expressiveness. Deep learning has been used to capture
non-linearities by learning deep item representations from side information but
as side information is high-dimensional existing deep models tend to have large
input dimensionality, which dominates their overall size. This makes them
difficult to train, especially with small numbers of inputs.
Rather than learning item representations, which is problematic with
high-dimensional side information, in this paper, we propose to learn feature
representation through deep learning from side information. Learning feature
representations, on the other hand, ensures a sufficient number of inputs to
train a deep network. To achieve this, we propose to simultaneously recover
user ratings and side information, by using a Variational Autoencoder (VAE).
Specifically, user ratings and side information are encoded and decoded
collectively through the same inference network and generation network. This is
possible as both user ratings and side information are data associated with
items. To account for the heterogeneity of user rating and side information,
the final layer of the generation network follows different distributions
depending on the type of information. The proposed model is easy to implement
and efficient to optimize and is shown to outperform state-of-the-art top-
recommendation methods that use side information.Comment: 7 pages, 3 figures, DLRS workshop 201
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
We present a general approach for collaborative filtering (CF) using spectral
regularization to learn linear operators from "users" to the "objects" they
rate. Recent low-rank type matrix completion approaches to CF are shown to be
special cases. However, unlike existing regularization based CF methods, our
approach can be used to also incorporate information such as attributes of the
users or the objects -- a limitation of existing regularization based CF
methods. We then provide novel representer theorems that we use to develop new
estimation methods. We provide learning algorithms based on low-rank
decompositions, and test them on a standard CF dataset. The experiments
indicate the advantages of generalizing the existing regularization based CF
methods to incorporate related information about users and objects. Finally, we
show that certain multi-task learning methods can be also seen as special cases
of our proposed approach
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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