Title: Search Intelligence: Deep Learning For Dominant Category Prediction,
Abstract: Deep Neural Networks, and specifically fully-connected convolutional neural
networks are achieving remarkable results across a wide variety of domains.
They have been trained to achieve state-of-the-art performance when applied to
problems such as speech recognition, image classification, natural language
processing and bioinformatics. Most of these deep learning models when applied
to classification employ the softmax activation function for prediction and aim
to minimize cross-entropy loss. In this paper, we have proposed a supervised
model for dominant category prediction to improve search recall across all eBay
classifieds platforms. The dominant category label for each query in the last
90 days is first calculated by summing the total number of collaborative clicks
among all categories. The category having the highest number of collaborative
clicks for the given query will be considered its dominant category. Second,
each query is transformed to a numeric vector by mapping each unique word in
the query document to a unique integer value; all padded to equal length based
on the maximum document length within the pre-defined vocabulary size. A
fully-connected deep convolutional neural network (CNN) is then applied for
classification. The proposed model achieves very high classification accuracy
compared to other state-of-the-art machine learning techniques.