68,191 research outputs found
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.Comment: to appear in ACM Computing Survey
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
Etsy is a global marketplace where people across the world connect to make,
buy and sell unique goods. Sellers at Etsy can promote their product listings
via advertising campaigns similar to traditional sponsored search ads.
Click-Through Rate (CTR) prediction is an integral part of online search
advertising systems where it is utilized as an input to auctions which
determine the final ranking of promoted listings to a particular user for each
query. In this paper, we provide a holistic view of Etsy's promoted listings'
CTR prediction system and propose an ensemble learning approach which is based
on historical or behavioral signals for older listings as well as content-based
features for new listings. We obtain representations from texts and images by
utilizing state-of-the-art deep learning techniques and employ multimodal
learning to combine these different signals. We compare the system to
non-trivial baselines on a large-scale real world dataset from Etsy,
demonstrating the effectiveness of the model and strong correlations between
offline experiments and online performance. The paper is also the first
technical overview to this kind of product in e-commerce context
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