117 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
Bridging Vision and Language over Time with Neural Cross-modal Embeddings
Giving computers the ability to understand multimedia content is one of the goals
of Artificial Intelligence systems. While humans excel at this task, it remains a challenge,
requiring bridging vision and language, which inherently have heterogeneous
computational representations. Cross-modal embeddings are used to tackle this challenge,
by learning a common space that uni es these representations. However, to grasp
the semantics of an image, one must look beyond the pixels and consider its semantic
and temporal context, with the latter being de ned by images’ textual descriptions and
time dimension, respectively. As such, external causes (e.g. emerging events) change the
way humans interpret and describe the same visual element over time, leading to the
evolution of visual-textual correlations.
In this thesis we investigate models that capture patterns of visual and textual interactions
over time, by incorporating time in cross-modal embeddings: 1) in a relative manner,
where by using pairwise temporal correlations to aid data structuring, we obtained a
model that provides better visual-textual correspondences on dynamic corpora, and 2) in
a diachronic manner, where the temporal dimension is fully preserved, thus capturing
visual-textual correlations evolution under a principled approach that jointly models
vision+language+time. Rich insights stemming from data evolution were extracted from
a 20 years large-scale dataset. Additionally, towards improving the e ectiveness of these
embedding learning models, we proposed a novel loss function that increases the expressiveness
of the standard triplet-loss, by making it adaptive to the data at hand. With our
adaptive triplet-loss, in which triplet speci c constraints are inferred and scheduled, we
achieved state-of-the-art performance on the standard cross-modal retrieval task
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