246 research outputs found
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
Learning to Make Predictions on Graphs with Autoencoders
We examine two fundamental tasks associated with graph representation
learning: link prediction and semi-supervised node classification. We present a
novel autoencoder architecture capable of learning a joint representation of
both local graph structure and available node features for the multi-task
learning of link prediction and node classification. Our autoencoder
architecture is efficiently trained end-to-end in a single learning stage to
simultaneously perform link prediction and node classification, whereas
previous related methods require multiple training steps that are difficult to
optimize. We provide a comprehensive empirical evaluation of our models on nine
benchmark graph-structured datasets and demonstrate significant improvement
over related methods for graph representation learning. Reference code and data
are available at https://github.com/vuptran/graph-representation-learningComment: Published as a conference paper at IEEE DSAA 201
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