標(biāo)題: Titlebook: Artificial Intelligence for Customer Relationship Management; Solving Customer Pro Boris Galitsky Book 2021 The Editor(s) (if applicable) a [打印本頁(yè)] 作者: 信賴(lài) 時(shí)間: 2025-3-21 17:00
書(shū)目名稱(chēng)Artificial Intelligence for Customer Relationship Management影響因子(影響力)
書(shū)目名稱(chēng)Artificial Intelligence for Customer Relationship Management影響因子(影響力)學(xué)科排名
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書(shū)目名稱(chēng)Artificial Intelligence for Customer Relationship Management網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Artificial Intelligence for Customer Relationship Management被引頻次
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書(shū)目名稱(chēng)Artificial Intelligence for Customer Relationship Management年度引用
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書(shū)目名稱(chēng)Artificial Intelligence for Customer Relationship Management讀者反饋
書(shū)目名稱(chēng)Artificial Intelligence for Customer Relationship Management讀者反饋學(xué)科排名
作者: 善辯 時(shí)間: 2025-3-21 21:17
978-3-030-61643-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Infusion 時(shí)間: 2025-3-22 02:00 作者: plasma 時(shí)間: 2025-3-22 04:33
Molecular fracture in polymers,We draw the conclusions for Volume 1 and 2 of this book.作者: 有權(quán) 時(shí)間: 2025-3-22 09:05
Conclusions,We draw the conclusions for Volume 1 and 2 of this book.作者: Coeval 時(shí)間: 2025-3-22 16:12 作者: 淡紫色花 時(shí)間: 2025-3-22 18:15
More on Exponential Representation,her conversations with other people, including customer support agents (CSA) and joins the conversation only when there is something important to recommend and the time is correct to do so. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and作者: 潰爛 時(shí)間: 2025-3-22 22:40 作者: AORTA 時(shí)間: 2025-3-23 03:02
Failure Rate and Mean Remaining Lifetime,finds documents, extracts topics from them, organizes these topics in clusters, receives from the user clarification on which cluster is most relevant, and provides the content for this cluster. This content can be provided in the form of a virtual dialogue so that the answers are derived from the f作者: granite 時(shí)間: 2025-3-23 08:39 作者: DEVIL 時(shí)間: 2025-3-23 11:12 作者: 藥物 時(shí)間: 2025-3-23 17:21 作者: 序曲 時(shí)間: 2025-3-23 19:52
Demographic and Biological Applications, concept learning technique for common scenarios of interaction between conflicting human agents. Customer complaints are classified as valid (requiring some kind of compensation) or invalid (requiring reassuring and calming down) the customer. Scenarios are represented by directed graphs with label作者: 周興旺 時(shí)間: 2025-3-24 00:40
Demographic and Biological Applications,d produces the consecutive mental states as plausible to a real-world scenario as possible. We simulate a multiagent decision-making environment taking into account intentions, knowledge and beliefs of itself and others. The simulation results are evaluated with respect to precision, completeness an作者: 草率男 時(shí)間: 2025-3-24 03:05
Holm Altenbach,Vladimir A. Kolupaevdepartment. We explore a technology that can detect this performance and a root cause for it, in terms of We explore the phenomenon of Distributed Incompetence (DI), which is an opposite to Distributed Knowledge and occurs in various organizations such as customer support. In a DI organization, a te作者: Contort 時(shí)間: 2025-3-24 07:09
Boris GalitskyIntroduces a number of dialogue management algorithms to drive a user through multiple ways of solving his problem.Explains how to detect misinformation, fake content and deception relying on discours作者: 橫截,橫斷 時(shí)間: 2025-3-24 12:36
Human–Computer Interaction Serieshttp://image.papertrans.cn/b/image/162361.jpg作者: 艦旗 時(shí)間: 2025-3-24 17:51
More on Exponential Representation,attempt to resolve?it, along with receiving a recommendation for a product with features addressing this problem. The performance of RJC is evaluated in a number of human–human and human-chatbot dialogues and demonstrates that RJC is an efficient and less intrusive way to provide high relevance and persuasive recommendations.作者: 良心 時(shí)間: 2025-3-24 22:42 作者: Gratulate 時(shí)間: 2025-3-25 02:39 作者: PRO 時(shí)間: 2025-3-25 05:40 作者: 伴隨而來(lái) 時(shí)間: 2025-3-25 11:28 作者: 可卡 時(shí)間: 2025-3-25 13:21 作者: 魯莽 時(shí)間: 2025-3-25 15:48
Adjusting Chatbot Conversation to User Personality and Mood, sources to properly react to the customer in the emotional space and to navigate him through it. We evaluated an overall contribution of a chatbot enabled with affective computing and observed up to 18% boost in the relevance of responses, as perceived by customers.作者: 聽(tīng)覺(jué) 時(shí)間: 2025-3-25 20:07 作者: 水獺 時(shí)間: 2025-3-26 02:52 作者: LAY 時(shí)間: 2025-3-26 07:32 作者: 無(wú)可爭(zhēng)辯 時(shí)間: 2025-3-26 08:42 作者: 寬容 時(shí)間: 2025-3-26 15:47
Recommendation by Joining a Human Conversation,her conversations with other people, including customer support agents (CSA) and joins the conversation only when there is something important to recommend and the time is correct to do so. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and作者: JEER 時(shí)間: 2025-3-26 19:54 作者: 王得到 時(shí)間: 2025-3-26 22:12
A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination,finds documents, extracts topics from them, organizes these topics in clusters, receives from the user clarification on which cluster is most relevant, and provides the content for this cluster. This content can be provided in the form of a virtual dialogue so that the answers are derived from the f作者: amenity 時(shí)間: 2025-3-27 01:21 作者: analogous 時(shí)間: 2025-3-27 05:29 作者: 苦笑 時(shí)間: 2025-3-27 12:39 作者: indoctrinate 時(shí)間: 2025-3-27 15:24 作者: hurricane 時(shí)間: 2025-3-27 20:23 作者: Vertical 時(shí)間: 2025-3-27 23:23
CRM Becomes Seriously Ill,department. We explore a technology that can detect this performance and a root cause for it, in terms of We explore the phenomenon of Distributed Incompetence (DI), which is an opposite to Distributed Knowledge and occurs in various organizations such as customer support. In a DI organization, a te作者: 閃光你我 時(shí)間: 2025-3-28 02:14
Book 2021social dialogues for various modalities of communication with a customer...After we learn to detect fake content, deception and hypocrisy, we examine the domain of customer complaints. We simulate mental states, attitudes and emotions of a complainant and try to predict his behavior. Having suggeste作者: 暴露他抗議 時(shí)間: 2025-3-28 09:14
A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination,e to follow another, or inappropriate, based on communicative discourse considerations. We then describe a chatbot performing advertising and social promotion (CASP) to assist in the automation of managing friends and other social network contacts. This agent employs a domain-independent natural lan作者: OUTRE 時(shí)間: 2025-3-28 11:18
Concluding a CRM Session,thoritative conclusive answer in an attempt to satisfy this user. We develop a technique to build a representation of a logical argument from discourse structure and to reason about it to confirm or reject this argument. Our evaluation also involves a machine learning approach and confirms that a hy作者: Addictive 時(shí)間: 2025-3-28 17:56 作者: reflection 時(shí)間: 2025-3-28 19:58
Reasoning for Resolving Customer Complaints,pter demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. The suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning作者: AROMA 時(shí)間: 2025-3-28 23:34
,Concept-Based Learning of Complainants’ Behavior, and takes advantage of a more accurate way of matching sequences of communicative actions. Scenario representation and comparative analysis techniques developed herein are applied to the classification of textual customer complaints as a CRM component. In order to estimate complaint validity, we ta作者: CLEFT 時(shí)間: 2025-3-29 04:09 作者: Mangle 時(shí)間: 2025-3-29 10:39 作者: Aggregate 時(shí)間: 2025-3-29 11:46
Artificial Intelligence for Customer Relationship ManagementSolving Customer Pro作者: TRACE 時(shí)間: 2025-3-29 18:47 作者: Disk199 時(shí)間: 2025-3-29 23:01
Demographic and Biological Applications,thoritative conclusive answer in an attempt to satisfy this user. We develop a technique to build a representation of a logical argument from discourse structure and to reason about it to confirm or reject this argument. Our evaluation also involves a machine learning approach and confirms that a hy作者: 自戀 時(shí)間: 2025-3-30 02:31
