5,456 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
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
A large amount of information exists in reviews written by users. This source
of information has been ignored by most of the current recommender systems
while it can potentially alleviate the sparsity problem and improve the quality
of recommendations. In this paper, we present a deep model to learn item
properties and user behaviors jointly from review text. The proposed model,
named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel
neural networks coupled in the last layers. One of the networks focuses on
learning user behaviors exploiting reviews written by the user, and the other
one learns item properties from the reviews written for the item. A shared
layer is introduced on the top to couple these two networks together. The
shared layer enables latent factors learned for users and items to interact
with each other in a manner similar to factorization machine techniques.
Experimental results demonstrate that DeepCoNN significantly outperforms all
baseline recommender systems on a variety of datasets.Comment: WSDM 201
Attentive Neural Architecture Incorporating Song Features For Music Recommendation
Recommender Systems are an integral part of music sharing platforms. Often
the aim of these systems is to increase the time, the user spends on the
platform and hence having a high commercial value. The systems which aim at
increasing the average time a user spends on the platform often need to
recommend songs which the user might want to listen to next at each point in
time. This is different from recommendation systems which try to predict the
item which might be of interest to the user at some point in the user lifetime
but not necessarily in the very near future. Prediction of the next song the
user might like requires some kind of modeling of the user interests at the
given point of time. Attentive neural networks have been exploiting the
sequence in which the items were selected by the user to model the implicit
short-term interests of the user for the task of next item prediction, however
we feel that the features of the songs occurring in the sequence could also
convey some important information about the short-term user interest which only
the items cannot. In this direction, we propose a novel attentive neural
architecture which in addition to the sequence of items selected by the user,
uses the features of these items to better learn the user short-term
preferences and recommend the next song to the user.Comment: Accepted as a paper at the 12th ACM Conference on Recommender Systems
(RecSys 18
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