150,485 research outputs found
Learning Fashion Compatibility with Bidirectional LSTMs
The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.Comment: ACM MM 1
Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation
Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a "top'' (e.g. a shirt) and a "bottom'' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain
Compatibility Family Learning for Item Recommendation and Generation
Compatibility between items, such as clothes and shoes, is a major factor
among customer's purchasing decisions. However, learning "compatibility" is
challenging due to (1) broader notions of compatibility than those of
similarity, (2) the asymmetric nature of compatibility, and (3) only a small
set of compatible and incompatible items are observed. We propose an end-to-end
trainable system to embed each item into a latent vector and project a query
item into K compatible prototypes in the same space. These prototypes reflect
the broad notions of compatibility. We refer to both the embedding and
prototypes as "Compatibility Family". In our learned space, we introduce a
novel Projected Compatibility Distance (PCD) function which is differentiable
and ensures diversity by aiming for at least one prototype to be close to a
compatible item, whereas none of the prototypes are close to an incompatible
item. We evaluate our system on a toy dataset, two Amazon product datasets, and
Polyvore outfit dataset. Our method consistently achieves state-of-the-art
performance. Finally, we show that we can visualize the candidate compatible
prototypes using a Metric-regularized Conditional Generative Adversarial
Network (MrCGAN), where the input is a projected prototype and the output is a
generated image of a compatible item. We ask human evaluators to judge the
relative compatibility between our generated images and images generated by
CGANs conditioned directly on query items. Our generated images are
significantly preferred, with roughly twice the number of votes as others.Comment: 9 pages, accepted to AAAI 201
A look at cloud architecture interoperability through standards
Enabling cloud infrastructures to evolve into a transparent platform while preserving integrity raises interoperability issues. How components are connected needs to be addressed. Interoperability requires standard data models and communication encoding technologies compatible with the existing Internet infrastructure. To reduce vendor lock-in situations, cloud computing must implement universal strategies regarding standards, interoperability and portability. Open standards are of critical importance and need to be embedded into interoperability solutions. Interoperability is determined at the data level as well as the service level. Corresponding modelling standards and integration solutions shall be analysed
The confusion about dietary fatty acids recommendations for CHD prevention
A recent meta-analysis of prospective cohort studies has not found an association between dietary saturated fat intake and CHD incidence. This funnelled the discussion about the importance of the recommendation to lower the intake of saturated fat for the prevention of CHD. At the same time a document of the European Food Safety Authority has suggested that specific quantitative recommendations are not needed for individual fatty acids but that more general statements can suffice. In this review, we discuss methodological aspects of the absence of association between SFA intake and CHD incidence in prospective cohort studies. We also summarise the results of the controlled dietary experiments on blood lipids and on CHD incidence in which saturated fat was replaced by either cis-unsaturated fat or carbohydrates. Finally, we propose a nutritionally adequate diet with an optimal fatty acid composition for the prevention of CHD in the context of dietary patterns. Such diets are characterised by a low intake of saturated fat, and as low as possible intake of trans-fat and fulfil the requirements for the intake of n-6 and n-3 fatty acids. No recommendation is needed for the intake of cis-MUF
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