2 research outputs found
Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval
Semantic similarity based retrieval is playing an increasingly important role
in many IR systems such as modern web search, question-answering, similar
document retrieval etc. Improvements in retrieval of semantically similar
content are very significant to applications like Quora, Stack Overflow, Siri
etc. We propose a novel unsupervised model for semantic similarity based
content retrieval, where we construct semantic flow graphs for each query, and
introduce the concept of "soft seeding" in graph based semi-supervised learning
(SSL) to convert this into an unsupervised model.
We demonstrate the effectiveness of our model on an equivalent question
retrieval problem on the Stack Exchange QA dataset, where our unsupervised
approach significantly outperforms the state-of-the-art unsupervised models,
and produces comparable results to the best supervised models. Our research
provides a method to tackle semantic similarity based retrieval without any
training data, and allows seamless extension to different domain QA
communities, as well as to other semantic equivalence tasks.Comment: Published in Proceedings of the 2017 ACM Conference on Information
and Knowledge Management (CIKM '17
Social Media Advertisement Outreach: Learning the Role of Aesthetics
Corporations spend millions of dollars on developing creative image-based
promotional content to advertise to their user-base on platforms like Twitter.
Our paper is an initial study, where we propose a novel method to evaluate and
improve outreach of promotional images from corporations on Twitter, based
purely on their describable aesthetic attributes. Existing works in aesthetic
based image analysis exclusively focus on the attributes of digital
photographs, and are not applicable to advertisements due to the influences of
inherent content and context based biases on outreach.
Our paper identifies broad categories of biases affecting such images,
describes a method for normalization to eliminate effects of those biases and
score images based on their outreach, and examines the effects of certain
handcrafted describable aesthetic features on image outreach. Optimizing on the
describable aesthetic features resulting from this research is a simple method
for corporations to complement their existing marketing strategy to gain
significant improvement in user engagement on social media for promotional
images.Comment: Accepted to SIGIR 201