Visual Search at eBay

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. A demonstration video can be found at https://youtu.be/iYtjs32vh4

Aspect Based Opinion Mining & Sentiment Analysis

Opinion mining is a relatively new field that refers to the practice of collecting feedback in the form of online reviews and ratings left by users on various topics. Researchers are now able to monitor the states of consciousness of individuals in real-time because to this development. Just lately, a number of research papers for sentiment analysis were implemented, each of which was based on a unique categorization and ranking procedure. However, the amount of time necessary for the newline performing class has not decreased in any way. Sentiment Sensitivity newline word list SST was provided as a solution to the problem of function mismatch in the go-domain sentiment class across the source area and the target domain; however, achieving improved accuracy and identifying distributional similarities of words became less effective as time went on. Hidden Markov’s persistent development may be seen at the beginning. Cosine In order to achieve more effective and clean pre-processing, a method that is conceptually quite similar to HM-CPCS has been devised. The HM-CPCS methodology, which has recently been suggested, makes use of the POS tagger, a variant of which is based on the Hidden Markov algorithm. Evaluations are created using data from a wide variety of different domains. Similar to a newline, the tags that come before and after it compute the possibility of transitions and the existence of the term newline among the tags in order to increase capability. This is done in order to improve capability

A Text Mining Based Approach for Mining Customer Attribute Data on Undefined Quality Problem

Understanding how the consumer perceives quality is a key issue in supply chain management. However, as the market structure continues to deepen, traditional evaluation methods using SEVRQUAL are unable identify all issues related to customer quality and unable to supply solutions. The maturation of data mining technology, however, has opened the possibilities of mining customer attribute data on quality problems from unstructured data. Based on the consumer perspective, this research uses an unsupervised machine learning text mining approach and the Recursive Neural Tensor Network to resolve the attribution process for undefined quality problems. It was found that the consumer quality perception system has a typical line-of-sight that can assist consumers quickly capture the logical structure of the quality problem. Although attributions related to quality problems are very scattered, a highly unified view was found to exist within each group, and a strategy to solve the undefined quality problem was agreed through group consensus by 61% of the consumers

Time-Varying Agglomeration Externalities in UK Counties between 1841 and 1971

Using dynamic panel data methods on UK counties (1841-1971), we investigate long-term employment dynamics in seven distinct local industries. We study how industries benefit from specialised environments (MAR), diverse local economies (Jacobs’) and large local markets (urbanization), and, in contrast to most other authors, test if the strength of MAR, Jacobs’ and urbanization externalities changes over time. We find declining MAR and rising Jacobs’ externalities since the mid-nineteenth century, questioning the adequacy of a static framework when studying agglomeration externalitiesagglomeration, dynamic externalities, Jacobs’ externalities, MAR externalities, urbanization externalities