Intelligent Customer Service Research Articles

The Development Potential and Challenges of One-Click Generative Artificial Intelligence in Cross-Border E-Commerce

This paper explores the development potential and challenges of one-click generative artificial intelligence (AI) in cross-border e-commerce. With the rapid growth of the Internet, cross-border e-commerce has seen significant market expansion, and the integration of AI technology presents new opportunities for the industry. This study analyzes the potential of one-click generative AI in cross-border e-commerce and its challenges. Reviewing relevant academic literature, the paper outlines key application scenarios of one-click generative AI, such as automatic product description generation and intelligent customer service assistants. Additionally, the study highlights the challenges associated with the technology, including issues related to semantic understanding and technological immaturity. The findings indicate that while one-click generative AI holds great potential for cross-border e-commerce, both technical and legal challenges must be addressed for its successful implementation.

Emotional dialogue generation model of electronic commerce intelligent customer service based on topic expansion

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience. In this paper, we applied artificial intelligence techniques, notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network. The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.

Multi-Modal Emotion Recognition Feature Extraction and Data Fusion Methods Evaluation

Research into emotion detection is crucial because of the wide range of fields that can benefit from it, including healthcare, intelligent customer service, and education. In comparison to unimodal approaches, multimodal emotion recognition (MER) integrates many modalities including text, facial expressions, and voice to provide better accuracy and robustness. This article provides a historical and present-day overview of MER, focusing on its relevance, difficulties, and approaches. We examine several datasets, comparing and contrasting their features and shortcomings; they include IEMOCAP and MELD. Recent developments in deep learning approaches, particularly fusion strategies such as early, late, and hybrid fusion are covered in the literature review. Data redundancy, complicated feature extraction, and real-time detection are among the identified shortcomings. Our suggested technique enhances emotion recognition accuracy by using deep learning to extract features using a hybrid fusion approach. To overcome existing restrictions and advance the area of MER, this study intends to direct future investigations in the right direction. Examining various data fusion strategies, reviewing new methodologies in multimodal emotion identification, and identifying problems and research needs to make up the primary body of this work.

Sentiments analysis for intelligent customer service dialogue using hybrid word embedding and stacking ensemble

Sentiments analysis for intelligent customer service dialogue using hybrid word embedding and stacking ensemble

Attention-based RNN with question-aware loss and multi-level copying mechanism for natural answer generation

Natural answer generation is in a very clear practical significance and strong application background, which can be widely used in the field of knowledge services such as community question answering and intelligent customer service. Traditional knowledge question answering is to provide precise answer entities and neglect the defects; namely, users hope to receive a complete natural answer. In this research, we propose a novel attention-based recurrent neural network for natural answer generation, which is enhanced with multi-level copying mechanisms and question-aware loss. To generate natural answers that conform to grammar, we leverage multi-level copying mechanisms and the prediction mechanism which can copy semantic units and predict common words. Moreover, considering the problem that the generated natural answer does not match the user question, question-aware loss is introduced to make the generated target answer sequences correspond to the question. Experiments on three response generation tasks show our model to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 0.727 BLEU on the SimpleQuestions response generation task, improving over the existing best results by over 0.007 BLEU. Our model has scored a significant enhancement on naturalness with up to 0.05 more than best performing baseline. The simulation results show that our method can generate grammatical and contextual natural answers according to user needs.

An Effective Data Fusion Methodology for Multi-modal Emotion Recognition: A Survey

Emotion recognition is a pivotal area of research with applications spanning education, healthcare, and intelligent customer service. Multimodal emotion recognition (MER) has emerged as a superior approach by integrating multiple modalities such as speech, text, and facial expressions, offering enhanced accuracy and robustness over unimodal methods. This paper reviews the evolution and current state of MER, highlighting its significance, challenges, and methodologies. We delve into various datasets, including IEMOCAP and MELD, providing a comparative analysis of their strengths and limitations. The literature review covers recent advancements in deep learning techniques, focusing on fusion strategies like early, late, and hybrid fusion. Identified gaps include issues related to data redundancy, feature extraction complexity, and real-time detection. Our proposed methodology leverages deep learning for feature extraction and a hybrid fusion approach to improve emotion detection accuracy. This research aims to guide future studies in addressing current limitations and advancing the field of MER. The main of this paper review recent methodologies in multimodal emotion recognition, analyze different data fusion techniques,identify challenges and research gaps.

Artificial Intelligence in Marketing

This thesis explores the application of artificial intelligence in marketing and the trends, challenges and opportunities of digital transformation. Firstly, it introduces the definition, development history and main technology and application areas of artificial intelligence. Then, it analyses the overview of the application of AI in the business field, including personalised recommendation, intelligent customer service and other aspects. Subsequently, how AI has changed marketing strategies and practices is explored and compared with traditional approaches. In terms of specific applications, the role and benefits of personalised recommendation systems, intelligent customer service systems, etc. are highlighted. In addition, the application of data-driven market trend analyses, intelligent predictive models, etc. in marketing is examined. Finally, the impact of AI and digital transformation on marketing is summarised, highlighting the need for companies to strengthen data security and management, and to grasp the opportunities of technological development to achieve sustainable development and competitive advantage.

Research on optimization design of intelligent customer service in e-commerce platform based on natural language processing and data mining

With the rapid development of artificial intelligence, machine learning, and deep learning, intelligent customer service systems have become a crucial component of automated customer service and intelligent management. This study conducts an in-depth investigation into the current status of intelligent customer service systems, highlighting the challenges faced by intelligent systems. Of particular concern is the significant challenge in generating meaningful, long-lasting, and information-rich customer service responses, despite the tremendous progress of artificial intelligence in many fields. To address this issue, based on a literature review, this study selects leading natural language processing technology (NVIDIA NeMo) and data mining tools (RISELab Ray). By combining these cutting-edge technologies, we propose innovative improvements and solutions aimed at enhancing the quality and efficiency of intelligent customer service systems.

Research on Recognition Method of Social Robot Based on T-A-GCNIIT in the Metaverse

Social robots are used in intelligent customer service, intelligent chat, intelligent shopping guides, and more because of emotion recognition studies in cognitive psychology. However, determining the user's purpose quickly and precisely has proved challenging. Domestic researchers proposed the A-GCNII model to address missing feature information; however, it needs a lot of math. This research offers a social robot recognition approach using the T-A-GCNIIT model and cognitive psychology to optimize computing complexity and performance. The T-A-GCNIIT algorithm processes social network data, and the Viola–Jones algorithm improves social robot intelligence to represent social robots in the meta-universe. The model performs well in node classification, link prediction, community discovery, and other tasks, with enhanced accuracy, recall, F1 score value, and other metrics. The model can also better comprehend the user's emotional state using cognitive psychology to better recognize their purpose and propose a fresh notion for enhancing social robots' cognitive psychology.

How Do Initial and Interactive Social Cues Increase Customers’ Continuance Usage Intention of Chatbots?

Enterprises widely utilize chatbots as “intelligent customer service” because of their efficiency and low cost. However, customer complaints and doubts occur occasionally, and improving users’ willingness to continue to use is a crucial issue that needs to be solved urgently. This study investigates whether and how initial social cues (avatar, name, and initial greeting) and interactive social cues (emoji) can improve users’ intention to continue using. Our findings from three scenario experiments reveal that both initial and interactive social cues can increase consumers’ warmth perception, while only interactive social cues elevate competence perception. The greater the warmth and competence perceived by users, the higher their trust in chatbots, leading to increased intention to continue using them. Furthermore, we identified a reinforcing effect when both types of social cues are combined, further augmenting users’ intention to persist with chatbots. This study contributes to the chatbot-human interaction literature by validating the effectiveness of these two social cues and offers practical insights for enhancing customer retention and optimizing chatbot design.

Research and Application of Large Model-Based Intelligent Customer Service System

With the rapid development of artificial intelligence technology, intelligent customer service systems have been widely used. This paper addresses the limitations of traditional intelligent customer service systems, such as limited language understanding ability, narrow knowledge coverage, and insufficient personalized service. It proposes an intelligent customer service system design scheme based on the RAG model. The scheme leverages the powerful language understanding and generation capabilities of large models, combined with dialogue management and knowledge base retrieval enhancement techniques, to build an efficient and intelligent customer service system. This paper introduces the overall architecture of the system, the design and implementation of each module, and comprehensively evaluates the system through experiments. The experimental results show that the system can provide accurate and fluent customer service, significantly improving customer satisfaction. The research in this paper provides new ideas and references for the development of intelligent customer service systems.

Multifactor synergistic influence of data intelligence on consumer mobile shopping

AbstractData intelligence (DI) is constantly influencing consumers. According to the theory of environmental psychology model, when consumers are stimulated by enterprise DI in mobile shopping, they can easily transform emotional reactions into corresponding behaviors. Based on this theory, this research adopts a hybrid method combining qualitative interview and fuzzy set qualitative comparative analysis (fsQCA) to explore the multifactor synergistic effects of DI factors on consumer emotions and mobile shopping intentions. Study 1 extracts five DI factors through qualitative study using NVivo software. All but one of the five DI factors had strong effects on consumer emotions. Study 2 uses the fsQCA method to analyze large‐sample survey data, revealing four configurations of DI factors that trigger positive consumer emotions and three that trigger mobile shopping intentions. The presence of intelligent search, intelligent recommendations, and intelligent logistics are the core conditions that trigger positive consumer emotions and mobile shopping intentions, while the presence or absence of intelligent customer service and intelligent pricing trigger positive consumer emotions and mobile shopping intentions. The research results supplement the existing theories and provide practical guidance for enterprises in DI stimulus design and government in DI environment construction.

IcsPLMs: Exploring Pre-trained Language Models in Intelligent Customer Service (Student Abstract)

Pre-trained language models have shown their high performance of text processing in intelligent customer service platforms. However, these models do not leverage domain specific information. In this paper, we propose icsPLMs optimized for intelligent customer service on both word and sentence levels. Our experimental results represent that using targeted strategies can further improve the performance of pre-trained language models in this field.

Research on Private Domain Traffic Operation Link Based on D2C Lead Marketing

This project aims to build an online platform, open up an integrated link integrating brand building, mental planting and effect transformation, obtain user leads through various marketing methods such as content creation and channel delivery, and establish a social customer relationship management system for the third-party automotive industry based on artificial intelligence technology. To carry out the link operation after retention collection, data cleaning, hierarchical screening, AI intelligent customer service, etc., to establish high-quality brand user clues database information. On the basis of exploring the realization of live streaming clues, this project will cooperate with the media operation room to build the mini program platform for users of Zhongqi Media, complete the introduction of the platform for prospective users of online and offline projects of Zhongqi Media, and conduct user operation through the modular construction of the platform. Build a complete private domain operation system of "public domain drainage, content planting, attracting and retaining funds, label classification, private domain precipitation, guided purchase, activation, potential customer tracking, and data duplication".

Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology

ABSTRACT Consumers have begun to move their attention away from product functioning and toward value probably extracted from items. Companies have begun to use customer service systems (CSS) in response to this trend, which are business models that give clients with not solitary tangible items as well as intangible facilities. Even with substantial investigation on Smart CSS frameworks, rare of this frameworks considered customers active data producers actively creating data for the Smart CSS. Furthermore, the majority of them offered a generic remedy rather than a personalized one. To classify customer service systems, performance metrics, such as precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, and RoC are considered. The performance of AETGCN-NGOA-CSS approach attains 19.11%, 24.12% and 28.13% high specificity, 24.93%, 23.04%, and 9.51% lower computation time, 15.2%, 25.45% and 13.91% higher ROC and 8.45%, 20.98%, and 27.55% higher accuracy compared with existing methods, such as developing personalized recommendation system in smart product service system depend on unsupervised learning model (CSS-BERT), Cognitive Decision-Making approaches in Data-driven Retail Intelligence: Consumer Sentiments, Choices, Shopping Behaviors (CSS-CDMA), e-Commerce Online Intelligent Customer Service System under Fuzzy Control (CSS-FFNN), respectively.

Intelligent Customer Service System Optimization Based on Artificial Intelligence

To elevate the intelligence of customer service dialogue systems, this article proposes an intelligent customer service system comprising chat dialogue subsystems, task-oriented multi-turn dialogue subsystems, single-turn dialogue subsystems, and an integration model. Firstly, to enhance diversity of responses and improve user experience, particularly in casual chat scenarios, this article presents a Seq2Seq-based approach for multi-answer responses, allowing for more expressive emotional expression in responses. Secondly, to address situations where customers cannot articulate their needs in a single sentence during multi-turn dialogues, this article designs a task-oriented multi-turn dialogue module. It employs intent recognition and slot filling to maintain contextual information throughout the conversation, aiding customers in problem resolution. Lastly, to overcome the current limitation of intelligent customer service models providing relatively one-dimensional answers in specific domains.

Research on AI Commercial Application development

As the emerging industry continues to grow, there has been a rise in both the quality and quantity of AI products during the era of commercialization. This article aims to discuss the pros and cons of AI products and looks ahead to their commercialized development. The primary focus of this paper is to examine the current state and progress of AI commercialization by analyzing four aspects: AI-generated artwork technology, machine translation capabilities, and intelligent customer service to AI itself such as its lack of intelligence, limitations in creativity, copyright concerns, and social impact. increasing involvement in human society, it becomes crucial to establish a new human-machine ecosystem in this era under specific circumstances. In terms of politics, the " China 2025" policy proposed a means to integrate information technology with manufacturing processes – aligning with the trajectory of AI commercialization.

Research on the Impact of Intelligent Customer Service on Trust Restoration in Service Recovery—Based on the Perspective of Customer Social Mindfulness Perception

Research on the Impact of Intelligent Customer Service on Trust Restoration in Service Recovery—Based on the Perspective of Customer Social Mindfulness Perception

Consumer behavior with Artificial intelligence products

In today's era, science and technology have made rapid progress, which makes the rapid formation of the Internet era, and promotes the wide application of artificial intelligence in the commercial field. With the rapid development of e-commerce, online shopping has become more and more popular, and online shopping has become the way of shopping chosen by more and more people. In the context of the continuous development of artificial intelligence, for the online shopping industry, actively citing artificial intelligence is very beneficial to the development of the industry. However, considering that consumers' acceptance of intelligent customer service is different, this artical will use Technology Acceptance Model to analyze the usefulness and ease of intelligent customer service, and confirm consumers' acceptance of intelligent customer service in the process of online consumption. The remainder of this paper is organized as follows.

Design of Intelligent Customer Service Knowledge Base for Medical Insurance Based on Foundation-scale Models

In this paper, we propose a method for constructing an intelligent customer service knowledge base system for medical insurance based on natural language processing (NLP) models. This method utilizes a local vector database to construct the knowledge base system and retrieves industry knowledge by calling the vector database through foundation-scale models. The use of this technology ensures the security of business data and reduces platform training costs. The intelligent knowledge base of medical insurance constructed using this method can automatically handle user’s inquiries, complaints, and general business transactions, effectively improving the efficiency and intelligence level of customer service.