6,611 research outputs found
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
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
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
In this paper, we propose a kinship generator network that can synthesize a
possible child face by analyzing his/her parent's photo. For this purpose, we
focus on to handle the scarcity of kinship datasets throughout the paper by
proposing novel solutions in particular. To extract robust features, we
integrate a pre-trained face model to the kinship face generator. Moreover, the
generator network is regularized with an additional face dataset and
adversarial loss to decrease the overfitting of the limited samples. Lastly, we
adapt cycle-domain transformation to attain a more stable results. Experiments
are conducted on Families in the Wild (FIW) dataset. The experimental results
show that the contributions presented in the paper provide important
performance improvements compared to the baseline architecture and our proposed
method yields promising perceptual results.Comment: Accepted to IEEE ICIP 201
Computational Intelligence for the Micro Learning
The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis.
The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results
Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent
Despite recommender systems play a key role in network content platforms,
mining the user's interests is still a significant challenge. Existing works
predict the user interest by utilizing user behaviors, i.e., clicks, views,
etc., but current solutions are ineffective when users perform unsettled
activities. The latter ones involve new users, which have few activities of any
kind, and sparse users who have low-frequency behaviors. We uniformly describe
both these user-types as "cold users", which are very common but often
neglected in network content platforms. To address this issue, we enhance the
representation of the user interest by combining his social interest, e.g.,
friendship, following bloggers, interest groups, etc., with the activity
behaviors. Thus, in this work, we present a novel algorithm entitled SocialNet,
which adopts a two-stage method to progressively extract the coarse-grained and
fine-grained social interest. Our technique then concatenates SocialNet's
output with the original user representation to get the final user
representation that combines behavior interests and social interests. Offline
experiments on Tencent video's recommender system demonstrate the superiority
over the baseline behavior-based model. The online experiment also shows a
significant performance improvement in clicks and view time in the real-world
recommendation system. The source code is available at
https://github.com/Social4Rec/SocialNet
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