Query: query classification ecommerce

title: Query Driven Data Labeling with Experts: Why Pay Twice?
abstract: Data has become a major priority for customer facing businesses of all sizes. Companies put a lot of effort and money into storing, cleaning, organizing, enriching and processing data to better meet user needs. Usually in large scale systems such as big ecommerce sites these tasks involve machine learning methods, relying on training data annotated by domain experts. Since domain experts are an expensive resource in terms of monetary costs and latency, it is desired to design algorithms that minimize the interaction with them. In this paper we address the problem of minimizing the number of annotation tasks with respect to a set of queries.We present a dedicated algorithm based on efficient labeling, that dictates the strategy for constructing a minimal set of classifiers sufficing to answer all queries. Our approach not only reduces monetary costs and latency, but also avoids data redundancy and saves storage space. We first consider a typical scenario of two expressions per query, and further discuss the challenges of extending our approach to multiple expressions. We examine two common models: batch and stream configurations, and devise offline and online algorithms, respectively. We analyze the number of annotations, and demonstrate the efficiency and effectiveness of our algorithm on a real world dataset.

title: Query Rewriting using Automatic Synonym Extraction for E commerce Search
abstract: Query rewriting is a critical component in modern search engines. It is the process of altering and enhancing raw user queries using synonymous keywords or structured metadata to improve search recall and relevance using data mining techniques applied on textual data and user behavioral signals. For example, the query bicycle is rewritten to match (bicycle OR bike) i.e. all items that either contain the word bicycle or bike in their title are returned for this query. Choosing the right set of synonymous terms for a given query term is essential to ensure the quality of search results, especially in the context of e commerce where buyer needs can be very specific. As an example, shoe is a good synonym for shoes, whereas sandals, while related, is not a good synonym for shoes. In this work, we describe one version of the approaches to query rewriting taken at eBay search. At a high level, we use a two step process to generate and apply synonyms for query expansions 1. offline token level synonym generation and 2. runtime search query rewriting. In the offline phase, we first generate a large pool of candidate synonyms for query tokens using various techniques leveraging user behavioral data, inventory and taxonomy information and open source knowledge bases; then, we leverage a machine learned binary classifier t rained o n h uman j udged b inary r elevance l abels t o filter the candidate synonyms that are truly useful as query expansions without compromising result set precision; this classifier allows us to leverage a wide variety of sources and techniques to generate synonym candidates by providing a scientific and scalable method to evaluate their effectiveness for query rewriting. This filtered set of token level synonyms is stored in a dictionary for runtime query rewriting. In the online phase, we rewrite user search queries by combining the token level synonyms in the dictionary, creating a boolean recall expression. We empirically demonstrate the value of this approach to enhance e commerce search recall and relevance.

title: Machine Learning for Web Page Classification: A Survey
abstract: The Internet contains a vast amount of data that is growing exponentially. To exploit this data, a Web information retrieval system and a categorization of internet content based on the classification of web pages are essential. Web page classification has many applications, among them the construction of web directories and the building of focused crawlers. In this paper, we present the characteristics of web page classification, we produce a literature review by summarizing and evaluating all sources related to web page classification crawled automatically from ScienceDirect and Springer websites, we review the different machine learning algorithms used to categorize web pages. Finally, we track the underlying assumptions behind the studied methods.

title: Anomaly Detection and Classification using Distributed Tracing and Deep Learning
abstract: Artificial Intelligence for IT Operations (AIOps) combines big data and machine learning to replace a broad range of IT Operations tasks including availability, performance, and monitoring of services. By exploiting log, tracing, metric, and network data, AIOps enable detection of faults and issues of services. The focus of this work is on detecting anomalies based on distributed tracing records that contain detailed information for the availability and the response time of the services. In large scale distributed systems, where a service is deployed on heterogeneous hardware and has multiple scenarios of normal operation, it becomes challenging to detect such anomalous cases. We address the problem by proposing unsupervised, response time anomaly detection based on deep learning data modeling techniques; unsupervised dynamic error threshold approach; tolerance module for false positive reduction; and descriptive classification of the anomalies. The evaluation shows that the approach achieves high accuracy and solid performance in both, experimental testbed and large scale production cloud.

title: ECOM'19: The SIGIR 2019 Workshop on eCommerce
abstract: eCommerce Information Retrieval is receiving increasing attention in the academic literature, and is an essential component of some of the largest web sites (such as eBay, Amazon, Airbnb, Alibaba, Taobao, Target, Facebook, Home Depot, and others) These kinds of organisations clearly value the importance of research into Information Retrieval. The purpose of this workshop is to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, to set a research agenda, to examine how to build data sets, and how evaluate algorithms for research into this fascinating topic. eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products) there is a click stream, but more importantly, there is often a buy stream. eCommerce problems are wide in scope and range from user interaction modalities through to dynamic updates of a rapidly changing collection on auction sites, and the experienceness of some products (such as Airbnb bookings) This workshop is a follow up to very successful workshops held at SIGIR 2017 and SIGIR 2018. This year we will be running a data challenge (sponsored by eBay) which will allow us to follow up on multiple aspects that were discussed in the previous workshops (in particular, deterministic rank orders and how to evaluate these)

title: Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)
abstract: The SIGIR 2019 Workshop on eCommerce (ECOM19) was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France. The purpose of the workshop was to serve as a platform for publication and discussion of Information Retrieval and NLP research and their applications in the domain of eCommerce. The workshop program was designed to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in the eCommerce domain. A second goal was to run a data challence on real world eCommerce data. The workshop drew contributions from both industry as well as academia, in total the workshop received 38 submissions, and accepted 24 (63% There were two keynotes by invited speakers, a poster session where all the accepted submissions were presented, a panel discussion, and three short talks by invited speakers.

title: Annotation Process for the Dialog Act Classification of a Taglish E commerce Q&A Corpus
abstract: With conversational agents or chatbots making up in quantity of replies rather than quality, the need to identify user intent has become a main concern to improve these agents. Dialog act (DA) classification tackles this concern, and while existing studies have already addressed DA classification in general contexts, no training corpora in the context of e commerce is available to the public. This research addressed the said insufficiency by building a text based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines. The SWBDDAMSL tagset for DA classification was modified to 28 tags fitting the categories applicable to e commerce conversations. The posts were annotated manually by three (3) human annotators and preprocessing techniques decreased the vocabulary size from 6,340 to 1,134. After analysis, the corpus was composed dominantly of single label posts, with 34% of the corpus having multiple intent tags. The annotated corpus allowed insights toward the structure of posts created with single to multiple intents.

title: A systematic literature review of blockchain based applications: Current status, classification and open issues
abstract: This work provides a systematic literature review of blockchain based applications across multiple domains. The aim is to investigate the current state of blockchain technology and its applications and to highlight how specific characteristics of this disruptive technology can revolutionise "business as usual" practices. To this end, the theoretical underpinnings of numerous research papers published in high ranked scientific journals during the last decade, along with several reports from grey literature as a means of streamlining our assessment and capturing the continuously expanding blockchain domain, are included in this review. Based on a structured, systematic review and thematic content analysis of the discovered literature, we present a comprehensive classification of blockchain enabled applications across diverse sectors such as supply chain, business, healthcare, IoT, privacy, and data management, and we establish key themes, trends and emerging areas for research. We also point to the shortcomings identified in the relevant literature, particularly limitations the blockchain technology presents and how these limitations spawn across different sectors and industries. Building on these findings, we identify various research gaps and future exploratory directions that are anticipated to be of significant value both for academics and practitioners.

title: Targeted Sentiment Classification with Knowledge Powered Attention Network
abstract: Targeted sentiment classification aims to identify the sentiment expressed towards some targets given context sentences, having great application value in social media, ecommerce platform and other fields. Most of the previous methods model context and target words with RNN and attention mechanism, which primarily do not use any external knowledge. In this paper, we utilize external knowledge from knowledge bases to reinforce the semantic representation of context and target. We propose a new model called Knowledge Powered Attention Network (KPAN) which uses the multi head attention mechanism to represent target and context and to fuse with conceptual knowledge extracted from external knowledge bases. The experiments on three public datasets revealed that our proposed model outperforms the state of the art methods, which signify the validity of our model.

title: Visual Ontology Based Query Approach for Data Access in Heterogeneous Expertise Environment: Application in PLM Biomedical Imaging
abstract: The volume and complexity of Biomedical Imaging (BMI) data can be handled by well known Product Lifecycle Management (PLM) solutions if a research study in this field is modeled as a cyclic process of four phases: study specifications; raw data acquisition; data processing and results publication. However, current PLM systems do not provide easy, flexible and user adapted data access, especially in the context of heterogeneity expertise environments such as BMI. This paper presents VAQUERO (VisuAlization and QUERy based Ontology) a visual ontology based data query approach, that aims at providing different kinds of users in the BMI field (common/ external, domain expert and technical users) with easy self access to their data stored in a PLM Teamcenter system.