41 research outputs found
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
For a company looking to provide delightful user experiences, it is of
paramount importance to take care of any customer issues. This paper proposes
COTA, a system to improve speed and reliability of customer support for end
users through automated ticket classification and answers selection for support
representatives. Two machine learning and natural language processing
techniques are demonstrated: one relying on feature engineering (COTA v1) and
the other exploiting raw signals through deep learning architectures (COTA v2).
COTA v1 employs a new approach that converts the multi-classification task into
a ranking problem, demonstrating significantly better performance in the case
of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a
novel deep learning architecture that allows for heterogeneous input and output
feature types and injection of prior knowledge through network architecture
choices. This paper compares these models and their variants on the task of
ticket classification and answer selection, showing model COTA v2 outperforms
COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B
test is conducted in a production setting validating the real-world impact of
COTA in reducing issue resolution time by 10 percent without reducing customer
satisfaction
Data Mining: How Popular Is It?
Data Mining is a process used in the industry, to facilitate decision making. As the name implies, large volumes of data is mined or sifted, to find useful information for decision making. With the advent of E-business, Data Mining has become more important to practitioners. The purpose of this paper is to find out the importance of Data Mining by looking at the different application areas that have used data mining for decision making
TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems
This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor,
which leverages the latest Transformers models and machine learning techniques
quickly assign issues within an organization, like customer support, help desk
and alike issue ticketing systems. The project provides functionality to 1)
assign an issue to the correct group, 2) assign an issue to the best resolver,
and 3) provide the most relevant previously solved tickets to resolvers. We
leverage one ticketing system sample dataset, with over 3k+ groups and over
10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a
79.0% top 5 accuracy on resolver suggestions. We hope this research will
greatly improve average issue resolution time on customer support, help desk,
and issue ticketing systems
A Conceptual Model of Recommender System for Algorithm Selection
Classifier selection process implies mastering a lot of background information on the dataset, the model and the algorithms in question. We suggest that a recommender system can reduce this effort by registering background information and the knowledge of the expert. In this study we propose such a system and take a first look on how it can be done. We compare various classifiers against different datasets and then come up with the most appropriate classifier for a particular dataset based on its unique characteristic
Safety functional requirements for “Robot Fleets for Highly effective Agriculture and Forestry Management”
This paper summarizes the steps to be followed in order to achieve a safety verified design of RHEA robots units. It provides a detailed description of current international standards as well as scientific literature related to safety analysis and fault detection and isolation. A large committee of partners has been involved in this paper, which may be considered as a technical committee for the revision of the progress of safety development throughout the progress of RHEA project. Partners related to agricultural machinery, automation, and application development declare the interest of providing a stable framework for bringing the safety verification level required to be able to commercial unmanned vehicles such as those described in the RHEA flee
Mining demand chain knowledge for new product development and marketing
[[abstract]]Many enterprises devote a significant portion of their budget to new product development (NPD) and marketing to make their products distinctive from those of competitors, and better fit the needs and wants of consumers. Hence, knowledge and feedback on customer demand and consumption experience has become an important information and asset for enterprises. This paper investigates the following research issues in a world leading bicycle brand/manufacture company, GIANT of Taiwan: what exactly are the customerspsila ldquofunctional needsrdquo and ldquowantsrdquo for bicycles? Does knowledge of the customers and the product itself reflect the needs of the market? Can product design and planning for production lines be integrated with the knowledge of customers and market channels? Can the knowledge of customers and market channels be transformed into knowledge assets of the enterprises during the stage of NPD? The a priori algorithm is a methodology of association rule for data mining, which is implemented for mining demand chain knowledge from channels (sales and maintenance) and customers. Knowledge extraction from data mining results is illustrated as knowledge patterns and rules in order to propose suggestions and solutions to the case firm for NPD and marketing.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]E