6,067 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
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Interpretable multi-hop reading comprehension (RC) over multiple documents is
a challenging problem because it demands reasoning over multiple information
sources and explaining the answer prediction by providing supporting evidences.
In this paper, we propose an effective and interpretable Select, Answer and
Explain (SAE) system to solve the multi-document RC problem. Our system first
filters out answer-unrelated documents and thus reduce the amount of
distraction information. This is achieved by a document classifier trained with
a novel pairwise learning-to-rank loss. The selected answer-related documents
are then input to a model to jointly predict the answer and supporting
sentences. The model is optimized with a multi-task learning objective on both
token level for answer prediction and sentence level for supporting sentences
prediction, together with an attention-based interaction between these two
tasks. Evaluated on HotpotQA, a challenging multi-hop RC data set, the proposed
SAE system achieves top competitive performance in distractor setting compared
to other existing systems on the leaderboard.Comment: Accepted to AAAI 202
Majorana Phase And Matter Effects In Neutrino Chiral Oscillation
Due to finite masses and mixing, for neutrinos propagation in space-time,
there is a chiral oscillation between left- and right- chiral neutrinos,
besides the usual oscillation between different generations. The probability of
chiral oscillation is suppressed by a factor of making the effect
small for relativistic neutrinos. However, for non-relativistic neutrinos, this
effects can be significant. In matter, the equation of motion is modified. When
neutrinos produced in weak interaction pass through the matter, the effective
energies are split into two different ones depending on the helicity of the
neutrino. This results in different oscillation behavior for neutrinos with
different helicity, in particular there is a new resonant effect related to the
helicity state of neutrino different than the usual MSW effect. For Majorana
neutrinos, chiral oscillation also depends on Majorana phases
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