74,776 research outputs found
Style Conditioned Recommendations
We propose Style Conditioned Recommendations (SCR) and introduce style
injection as a method to diversify recommendations. We use Conditional
Variational Autoencoder (CVAE) architecture, where both the encoder and decoder
are conditioned on a user profile learned from item content data. This allows
us to apply style transfer methodologies to the task of recommendations, which
we refer to as injection. To enable style injection, user profiles are learned
to be interpretable such that they express users' propensities for specific
predefined styles. These are learned via label-propagation from a dataset of
item content, with limited labeled points. To perform injection, the condition
on the encoder is learned while the condition on the decoder is selected per
explicit feedback. Explicit feedback can be taken either from a user's response
to a style or interest quiz, or from item ratings. In the absence of explicit
feedback, the condition at the encoder is applied to the decoder. We show a 12%
improvement on NDCG@20 over the traditional VAE based approach and an average
22% improvement on AUC across all classes for predicting user style profiles
against our best performing baseline. After injecting styles we compare the
user style profile to the style of the recommendations and show that injected
styles have an average +133% increase in presence. Our results show that style
injection is a powerful method to diversify recommendations while maintaining
personal relevance. Our main contribution is an application of a
semi-supervised approach that extends item labels to interpretable user
profiles.Comment: 9 pages, 10 figures, Accepted to RecSys '1
Item Recommendation with Evolving User Preferences and Experience
Current recommender systems exploit user and item similarities by
collaborative filtering. Some advanced methods also consider the temporal
evolution of item ratings as a global background process. However, all prior
methods disregard the individual evolution of a user's experience level and how
this is expressed in the user's writing in a review community. In this paper,
we model the joint evolution of user experience, interest in specific item
facets, writing style, and rating behavior. This way we can generate individual
recommendations that take into account the user's maturity level (e.g.,
recommending art movies rather than blockbusters for a cinematography expert).
As only item ratings and review texts are observables, we capture the user's
experience and interests in a latent model learned from her reviews, vocabulary
and writing style. We develop a generative HMM-LDA model to trace user
evolution, where the Hidden Markov Model (HMM) traces her latent experience
progressing over time -- with solely user reviews and ratings as observables
over time. The facets of a user's interest are drawn from a Latent Dirichlet
Allocation (LDA) model derived from her reviews, as a function of her (again
latent) experience level. In experiments with five real-world datasets, we show
that our model improves the rating prediction over state-of-the-art baselines,
by a substantial margin. We also show, in a use-case study, that our model
performs well in the assessment of user experience levels
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
Retrospective analysis of chronic injuries in recreational and competitive surfers:Injury location, type, and mechanism
Only two studies have reported on chronic musculoskeletal surfing injuries. They found over half of the injuries were non-musculoskeletal, but did not consider mechanisms of injury. This study identified the location, type, and mechanisms of chronic injury in Australian recreational and competitive surfers using a crosssectional retrospective observational design. A total of 1,348 participants (91.3% males, 43.1% competitive surfers) reported 1,068 chronic injuries, 883 of which were classified as major. Lower back (23.2%), shoulder (22.4%), and knee (12.1%) regions had the most chronic injuries. Competitive surfers had significantly (p \u3c .05) more lower back, ankle/foot, and head/face injuries than recreational surfers. Injuries were mostly musculoskeletal with only 7.8% being of non-musculoskeletal origin. Prolonged paddling was the highest frequency (21.1%) for mechanism of injury followed by turning maneuvers (14.8%). The study results contribute to the limited research on chronic surfing injuries
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