2,062 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
Interaction-aware Factorization Machines for Recommender Systems
Factorization Machine (FM) is a widely used supervised learning approach by
effectively modeling of feature interactions. Despite the successful
application of FM and its many deep learning variants, treating every feature
interaction fairly may degrade the performance. For example, the interactions
of a useless feature may introduce noises; the importance of a feature may also
differ when interacting with different features. In this work, we propose a
novel model named \emph{Interaction-aware Factorization Machine} (IFM) by
introducing Interaction-Aware Mechanism (IAM), which comprises the
\emph{feature aspect} and the \emph{field aspect}, to learn flexible
interactions on two levels. The feature aspect learns feature interaction
importance via an attention network while the field aspect learns the feature
interaction effect as a parametric similarity of the feature interaction vector
and the corresponding field interaction prototype. IFM introduces more
structured control and learns feature interaction importance in a stratified
manner, which allows for more leverage in tweaking the interactions on both
feature-wise and field-wise levels. Besides, we give a more generalized
architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to
capture higher-order interactions. To further improve both the performance and
efficiency of IFM, a sampling scheme is developed to select interactions based
on the field aspect importance. The experimental results from two well-known
datasets show the superiority of the proposed models over the state-of-the-art
methods
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