9,299 research outputs found
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images
Finding clothes that fit is a hot topic in the e-commerce fashion industry.
Most approaches addressing this problem are based on statistical methods
relying on historical data of articles purchased and returned to the store.
Such approaches suffer from the cold start problem for the thousands of
articles appearing on the shopping platforms every day, for which no prior
purchase history is available. We propose to employ visual data to infer size
and fit characteristics of fashion articles. We introduce SizeNet, a
weakly-supervised teacher-student training framework that leverages the power
of statistical models combined with the rich visual information from article
images to learn visual cues for size and fit characteristics, capable of
tackling the challenging cold start problem. Detailed experiments are performed
on thousands of textile garments, including dresses, trousers, knitwear, tops,
etc. from hundreds of different brands.Comment: IEEE Conference on Computer Vision and Pattern Recognition Workshop
(CVPRW) 2019 Focus on Fashion and Subjective Search - Understanding
Subjective Attributes of Data (FFSS-USAD
- …