Deep learning on information retrieval using agent flow e-mail reply system for IoT enterprise customer service

Electronic Commerce (e-commerce) is the process of buying, selling orexchanging products, services and information via computer networks. e- commerce is part of the e-business, where the scope of e-business more broadly, not just commercial but also include business partners, customer service, job vacancies. In addition to networking technologies www, e-commerce technology also requires a database or a database (database), e-mail or electronic mail (e- mail), and the form of non-computer technology as well as other delivery systems, and means of payment for e -Commerce. Given the electronic commerce (e- commerce) is then the customer can access and perform orders from various places. Given the current era of advanced technology is the customer who wants to access e-commerce does not have to be somewhere, it is because in the big cities in Indonesia have a lot of places that provide an internet access facility using only the laptop / notebook or by Personal Digital Assistant (PDA) using wifi technology. Thus the time is now very necessary and desirable companies to implement e-commerceservices. The use of e-commerce in Indonesia is still very limited. From the background that there is then the author will discuss how e- commerce pemanfaaatan in their business interests.

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Predicting components for issue reports using deep learning with information retrieval

Proceedings of the 40th International Conference on Software Engineering Companion Proceeedings - ICSE '18 ◽

10.1145/3183440.3194952 ◽

2018 ◽

Cited By ~ 1

Author(s):

Morakot Choetkiertikul ◽

Hoa Khanh Dam ◽

Truyen Tran ◽

Trang Pham ◽

Aditya Ghose

Keyword(s):

Information Retrieval ◽

Deep Learning ◽

Issue Reports

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BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

Journal of Science and Technology - IUH ◽

10.46242/jst-iuh.v47i05.761 ◽

2021 ◽

Vol 47 (05) ◽

Author(s):

NGUYỄN CHÍ HIẾU

Keyword(s):

Information Retrieval ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Question Answering ◽

Semantic Analysis ◽

Knowledge Graph ◽

Question Answering Systems ◽

Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents. We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

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Digital Marketing and E-Tailing Technological Innovations

Advances in Finance, Accounting, and Economics - Avatar-Based Models, Tools, and Innovation in the Digital Economy ◽

10.4018/978-1-7998-1104-6.ch003 ◽

2020 ◽

pp. 37-65

Author(s):

Alan D. Smith

Keyword(s):

Data Mining ◽

Quality Assurance ◽

Big Data ◽

Information Retrieval ◽

Customer Service ◽

Middle Level ◽

Purchase Decision ◽

Product Reviews ◽

Privacy Concerns ◽

Web Presence

In an age of public mistrust of the most basic institutions, businesses are not exempted. Essentially all e-tailers want to deliver personalized and real-time communications to customers that are tailored to their interests and preferences, and are based on big data mining that customers will value over privacy concerns. This is an era in which e-commerce retailers continue to dominate the marketplace and it is integral that consumers are able to trust the manufacturers, retailers, and the service/product reviews that they read online. Such trust is particularly important if their ultimate purchase decision is a successful one. A survey of middle-level managers was analyzed to identity the basic elements: e-personalization, namely online purchasing behaviors, personalized communications, information-retrieval services, degree of personal web presence, quality assurance of customer service, and the promotion of customization services. These elements were found to be conceptually and statistically related to retailer benefits of increased buying and customer loyalty.

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Theoretical Framework for CRM Outsourcing

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch505 ◽

2005 ◽

pp. 2841-2845

Author(s):

Babita Gupta ◽

Lakshmi Iyer

Keyword(s):

Customer Service ◽

Customer Relationship Management ◽

Call Centers ◽

Relationship Management ◽

Theoretical Framework ◽

Customer Relationship ◽

Retail Stores ◽

Web Based ◽

Business Requirements ◽

E Mail

Customer service is emerging as a key differentiator among competitors as the explosive growth of e-commerce is changing the nature of competition among companies. This has changed the customer-related business requirements for all types of companies. With firms increasing their online operations, customers now have the ability to contact organizations through a variety of interactive and noninteractive means (such as e-mail, fax, call centers, FAQs, online chats, newsletters, snail mail, retail stores, and Web-based forums). This has led companies to consider customer relationship management (CRM) as an important part of their competitive strategy. As the focus is shifting to retention rather than acquisition of customers, companies are looking for ways to identify and engage their most profitable customers.

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Reply Using Past Replies—A Deep Learning-Based E-Mail Client

Electronics ◽

10.3390/electronics9091353 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1353

Author(s):

Yiwei Feng ◽

M. Asif Naeem ◽

Farhaan Mirza ◽

Ali Tahir

Keyword(s):

Deep Learning ◽

Hybrid Model ◽

Learning Algorithm ◽

Inverse Document Frequency ◽

Help Desk ◽

Deep Learning Algorithm ◽

Document Frequency ◽

Text Prediction ◽

E Mail ◽

Gated Recurrent Unit

Email is the most common and effective source of communication for most enterprises and individuals. In the corporate sector the volume of email received daily is significant while timely reply of each email is important. This generates a huge amount of work for the organisation, in particular for the staff located in the help-desk role. In this paper we present a novel Smart E-mail Management System (SEMS) for handling the issue of E-mail overload. The Term Frequency-Inverse Document Frequency (TF-IDF) model was used for designing a Smart Email Client in previous research. Since TF-IDF does not consider semantics between words, the replies suggested by the model are not very accurate. In this paper we apply Document to Vector (Doc2Vec) and introduce a novel Gated Recurrent Unit Sentence to Vector (GRU-Sent2Vec), which is a hybrid model by combining GRU and Sent2Vec. Both models are more intelligent as compared to TF-IDF. We compare our results from both models with TF-IDF. The Doc2Vec model performs the best on predicting a response for a similar new incoming Email. In our case, since the dataset is too small to require a deep learning algorithm model, the GRU-Sent2Vec hybrid model cannot produce ideal results, whereas in our understanding it is a robust method for long-text prediction.

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Sentiment Analysis of Fast Food Companies With Deep Learning Models

The Computer Journal ◽

10.1093/comjnl/bxaa131 ◽

2020 ◽

Author(s):

Ghazi Abdalla ◽

Fatih Özyurt

Keyword(s):

Deep Learning ◽

Data Collection ◽

Sentiment Analysis ◽

Customer Service ◽

Fast Food ◽

Training Data ◽

Experimental Result ◽

Learning Techniques ◽

Daunting Task ◽

Quality Of Products

Abstract In the modern era, Internet usage has become a basic necessity in the lives of people. Nowadays, people can perform online shopping and check the customer’s views about products that purchased online. Social networking services enable users to post opinions on public platforms. Analyzing people’s opinions helps corporations to improve the quality of products and provide better customer service. However, analyzing this content manually is a daunting task. Therefore, we implemented sentiment analysis to make the process automatically. The entire process includes data collection, pre-processing, word embedding, sentiment detection and classification using deep learning techniques. Twitter was chosen as the source of data collection and tweets collected automatically by using Tweepy. In this paper, three deep learning techniques were implemented, which are CNN, Bi-LSTM and CNN-Bi-LSTM. Each of the models trained on three datasets consists of 50K, 100K and 200K tweets. The experimental result revealed that, with the increasing amount of training data size, the performance of the models improved, especially the performance of the Bi-LSTM model. When the model trained on the 200K dataset, it achieved about 3% higher accuracy than the 100K dataset and achieved about 7% higher accuracy than the 50K dataset. Finally, the Bi-LSTM model scored the highest performance in all metrics and achieved an accuracy of 95.35%.

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An ensemble information retrieval method for the biomedical domain (Preprint)

10.2196/preprints.28272 ◽

2021 ◽

Author(s):

Zhiqiang Liu ◽

Jingkun Feng ◽

Zhihao Yang ◽

Lei Wang

Keyword(s):

Information Retrieval ◽

Deep Learning ◽

Text Classification ◽

Query Expansion ◽

Ensemble Method ◽

Classification Model ◽

Retrieval Performance ◽

Matching Model ◽

Ranking List ◽

Initial Retrieval

BACKGROUND With the development of biomedicine, the number of biomedical documents has increased rapidly, which brings a great challenge for researchers retrieving the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in some specific retrieval tasks and thereby increases the difficulty of biomedical information retrieval. OBJECTIVE This study aims to find a more systematic method to retrieve relevant scientific literature for a given patient. METHODS In the initial retrieval stage, we supplement query terms through query expansion strategies and apply query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employ a text classification model and relevance matching model to evaluate documents respectively from different dimensions, then we combine the outputs through logistic regression to re-rank all the documents from the initial ranking list. RESULTS The proposed ensemble method contributes to the improvement of biomedical retrieval performance. Comparing with the existing deep learning-based methods, experimental results show that our method achieves state-of-the-art performance on the data collection provided by TREC 2019 Precision Medicine Track. CONCLUSIONS In this paper, we propose a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and the relevance matching model can better capture semantic context information and improve retrieval performance.

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