scholarly journals Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13973-13974
Author(s):  
Riheng Yao ◽  
Shuangyong Song ◽  
Qiudan Li ◽  
Chao Wang ◽  
Huan Chen ◽  
...  
Keyword(s):  
Customer Service ◽  
Real World ◽  
User Satisfaction ◽  
Experimental Results ◽  
Practical Significance ◽  
Proposed Model ◽  
Questions And Answers ◽  
The Relationship

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

Study on the Permeability Model of the Porous Concrete

2011 ◽  
Vol 374-377 ◽  
pp. 1369-1373 ◽  
Author(s):  
Fang Ran Zhao ◽  
Mian Mian Chen ◽  
Zhao Lu Ding
Keyword(s):  
Pore Structure ◽  
Test Piece ◽  
Experimental Results ◽  
Effective Model ◽  
Permeability Model ◽  
Porous Concrete ◽  
Structure Changes ◽  
Proposed Model ◽  
Capillary Bundle ◽  
The Relationship

This paper presents an effective model which can equivalently regard the pores in the porous concrete as a series of capillary bundles with the same diameter due to the complexity of the pores in the porous concrete. The proposed model was used to calculated the total flows of all capillary bundles in the test piece by the single capillary bundle calculation approach from the Hagen-Poiseuille theorem, and together with the well known Darcy theorem to determine the relationship among the flow of the porous concrete, the diameter of pore and the minus of up and down water pressures. The experimental results show that the permeability of the porous concrete mainly depends on the pores with diameter more than 4.12mm, and the pore structure changes obviously when the valid porosity of the porous concrete is greater than 25%.

scholarly journals 3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces

2018 ◽  
Author(s):  
Liang Yang ◽  
Yuanfang Guo ◽  
Di Jin ◽  
Huazhu Fu ◽  
Xiaochun Cao
Keyword(s):  
Real World ◽  
Posterior Distribution ◽  
State Of The Art ◽  
Variational Inference ◽  
Experimental Results ◽  
Generation Process ◽  
Network Embedding ◽  
Topological Information ◽  
Proposed Model ◽  
Types Of Information

Combinational  network embedding, which learns the node representation by exploring both  topological and non-topological information, becomes popular due to the fact that the two types of information are complementing each other.  Most of the existing methods either consider the  topological and non-topological  information being aligned or possess predetermined preferences during the embedding process.Unfortunately, previous methods  fail to either explicitly describe the correlations between topological and non-topological information or adaptively weight their impacts. To address the existing issues, three new assumptions are proposed to better describe the embedding space and its properties. With the proposed assumptions, nodes, communities and topics are mapped into one embedding space. A novel generative model is proposed to formulate the generation process of the network and content from the embeddings, with respect to the Bayesian framework. The proposed model automatically leans to the information which is more discriminative.The embedding result can be obtained by maximizing the posterior distribution by adopting the variational inference and reparameterization trick. Experimental results indicate that the proposed method gives superior performances compared to the state-of-the-art methods when a variety of real-world networks is analyzed.

Multiple time period imputation technique for multiple missing traffic variables: nonparametric regression approach

2012 ◽  
Vol 39 (4) ◽  
pp. 448-459 ◽  
Author(s):  
Hyunho Chang ◽  
Dongjoo Park ◽  
Younginn Lee ◽  
Byoungjo Yoon
Keyword(s):  
Real World ◽  
Moving Average ◽  
Real Data ◽  
Multiple Time ◽  
Real World Data ◽  
Time Period ◽  
Proposed Model ◽  
Auto Regressive ◽  
Missing Variables ◽  
The Relationship

The objective of this study is to introduce an effective and practical model, based on non-parametric regression, to instantaneously estimate multivariate imputations replacing multiple missing variables during multiple time periods. The developed model was essentially designed for system-oriented, real-world applications. In an empirical study with real-world data, the proposed model, on the whole, outperformed the seasonal auto-regressive integrated moving average (ARIMA). The analysis of the results indicates that the introduced model was more applicable to multivariate imputation during multiple time intervals than that of ARIMA. In addition, it was revealed that ARIMA could somewhat deform the relationship between the volume (q) and speed (s), whereas the developed model reproduced the q–s relationship more similarly than ARIMA. Moreover, the proposed model is very simple and does not require system operators to input or recalibrate any external parameters because it was developed for applications of real data management systems.

scholarly journals A Data-Driven Emotion Model for English Learners Based on Machine Learning

2021 ◽  
Vol 16 (08) ◽  
pp. 34
Author(s):  
Zhao Zheng ◽  
Kew Si Na
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Facial Expression ◽  
English Learners ◽  
Experimental Results ◽  
Data Driven ◽  
Proposed Model ◽  
Research Findings ◽  
Emotion Model ◽  
The Relationship

Learning confusion is a common emotion among learners. With the aid of machine learning, this paper develops a data-driven emotion model that automatically recognizes learning confusion in facial expression images. The data on learning behaviors and learning confusion of multiple subjects were collected through an online English evaluation experiment, and imported to the proposed model to derive the relationship between learning confusion and academic performance, which is measured by the correctness of the students’ answers to the test questions. The experimental results show that the students with learning confusion had relatively low correct rate of answering test questions. The research findings reveal the relationship between learning confusion and academic performance, laying the basis for predicting the academic performance of English learners through machine learning.

scholarly journals Identification of Micro-blog Opinion Leaders based on User Features and Outbreak Nodes

2017 ◽  
Vol 12 (01) ◽  
pp. 141
Author(s):  
Lin Cui ◽  
Dechang Pi
Keyword(s):  
Real World ◽  
Latent Variable ◽  
Probability Model ◽  
Opinion Leader ◽  
Experimental Results ◽  
Opinion Leaders ◽  
Proposed Model ◽  
Input Variables ◽  
Real World Datasets ◽  
The Ideal

At present, recognition of micro-blog opinion leaders mainly depends on the number of users posting micro-blogs, registration time, the number of good friends and other static attributes. However, it is very difficult to obtain the ideal recognition results through the above mentioned methods. This paper puts forward a new method that identifies the opinion leaders according to the change of user features and outbreak nodes. Deeply analyzing various attributes and behaviors of users, on the basis of user features and outbreak nodes, user’s attribute features are regarded as the input variables, behavior features of the user and outbreak nodes are regarded as observed variables. The probability as an opinion leader is the latent variable between input variables and observation variables, and the constructed probability model is used to recognize micro-blog opinion leaders. Experiments are carried out on the two real-world datasets from Sina micro-blog and Twitter, and the comparative experimental results show that the proposed model can more precisely find the micro-blog opinion leaders.

scholarly journals Facebook’s power: factors influencing followers’ visit intentions

2019 ◽  
Vol 23 (1) ◽  
pp. 95-117 ◽  
Author(s):  
Francisca Blasco-Lopez ◽  
Nuria Recuero Virto ◽  
Joaquin Aldas Manzano ◽  
Daniela Cruz Delgado
Keyword(s):  
Customer Service ◽  
Online Survey ◽  
Information Quality ◽  
Group Analysis ◽  
Content Type ◽  
Frida Kahlo ◽  
Proposed Model ◽  
Two Samples ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to examine the role that Facebook Fan Pages (FFPs) play in the generation of visit intention. The study has three objectives: first, to examine the effects of museum-generated content (MGC) on perceived information quality and perceived customer service and perceived information quality and perceived customer service on visit intention and, second, to test the model with two samples to make comparisons that provide useful insights. Design/methodology/approach Data were collected through an online survey that achieved 308 valid responses. A multi-group analysis was conducted to compare the results from two groups: users of the Frida Kahlo museum and Anahuacalli museum FFPs. Findings The results reveal that there are significant differences between the two samples regarding the direct effects of perceived information quality on visit intention and perceived customer service on visit intention. The authors also noted a slight difference between the two museums’ FFPs in the relationship between MGC and perceived information quality. Research limitations/implications Further research is needed to examine other FFP factors that influence visit intention to clarify the results obtained from the two samples and to analyse the proposed model in other settings. This research contributes to the literature concerning the impact of online platforms on visit intention. Originality/value The findings provide useful insights for managers as to how to increase their FFP followers’ intention to visit their establishments.

scholarly journals A dynamic goal adapted task oriented dialogue agent

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249030
Author(s):  
Abhisek Tiwari ◽  
Tulika Saha ◽  
Sriparna Saha ◽  
Shubhashis Sengupta ◽  
Anutosh Maitra ◽  
...  
Keyword(s):  
Success Rate ◽  
Real World ◽  
User Satisfaction ◽  
End Users ◽  
Primary Objective ◽  
Experimental Results ◽  
Virtual Agents ◽  
Minimum Requirements ◽  
Dialogue Manager ◽  
Task Oriented

Purpose Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. Methodology Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serving action. User sentiment appears to be an appropriate indicator for goal discrepancy that guides the agent to complete the user’s desired task with gratification. The negative sentiment expressed by the user about an aspect of the provided choice is treated as a discrepancy that is being resolved by the GDM depending upon the observed discrepancy and current dialogue state. The goal update capability and the VA’s interactiveness trait enable end-users to accomplish their desired task satisfactorily. Findings The obtained experimental results illustrate that DGDVA can handle dynamic goals with maximum user satisfaction and a significantly higher success rate. The interaction drives the user to decide its final goal through the latent specification of possible choices and information retrieved and provided by the dialogue agent. Through the experimental results (qualitative and quantitative), we firmly conclude that the proposed sentiment-aware VA adapts users’ dynamic behavior for its goal setting with substantial efficacy in terms of primary objective i.e., task success rate (0.88). Practical implications In real world, it can be argued that many people do not have a predefined and fixed goal for tasks such as online shopping, movie booking & restaurant booking, etc. They tend to explore the available options first which are aligned with their minimum requirements and then decide one amongst them. The DGDVA provides maximum user satisfaction as it enables them to accomplish a dynamic goal that leads to additional utilities along with the essential ones. Originality To the best of our knowledge, this is the first effort towards the development of A Dynamic Goal Adapted Task-Oriented Dialogue Agent that can serve user goals dynamically until the user is satisfied.

scholarly journals Personalized Multimedia Item and Key Frame Recommendation

2019 ◽  
Author(s):  
Le Wu ◽  
Lei Chen ◽  
Yonghui Yang ◽  
Richang Hong ◽  
Yong Ge ◽  
...  
Keyword(s):  
General Problem ◽  
Real World ◽  
Experimental Results ◽  
Visual Images ◽  
Key Frame ◽  
Fine Grained ◽  
Interaction Behavior ◽  
Proposed Model ◽  
Latent Space ◽  
Visual Preferences

When recommending or advertising items to users, an emerging trend is to present each multimedia item with  a key frame image (e.g., the poster of a movie). As each multimedia item can be represented as  multiple fine-grained  visual images (e.g., related images of the movie), personalized key frame recommendation is necessary in these applications to attract users' unique visual preferences. However, previous personalized key frame recommendation models relied on users' fine grained image  behavior of  multimedia items (e.g., user-image interaction behavior), which is often not available in real scenarios.  In this paper, we study the general problem of joint multimedia item and key frame recommendation in the absence of the fine-grained user-image behavior. We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models  would fail without any users' fine-grained image behavior. To tackle this challenge, we leverage users' item behavior by projecting users(items) in two latent spaces: a collaborative latent space and a visual latent space. We further design a model to discern both the collaborative and  visual dimensions of users, and model how users make decisive item preferences from these two spaces. As a result, the learned user visual profiles could be directly applied for key frame recommendation. Finally, experimental results on a real-world dataset clearly show the effectiveness of our proposed model on the two recommendation tasks.

The Influence of Cognitive and Affective Reactions to Feedback on Subsequent Goals

2010 ◽  
Vol 15 (2) ◽  
pp. 121-131 ◽  
Author(s):  
Remus Ilies ◽  
Timothy A. Judge ◽  
David T. Wagner
Keyword(s):  
Performance Feedback ◽  
Business School ◽  
Structural Equation ◽  
Emotional Reactions ◽  
Equation Model ◽  
Proposed Model ◽  
And Performance ◽  
Dispositional Measure ◽  
The Relationship ◽  
Reactions To Feedback

This paper focuses on explaining how individuals set goals on multiple performance episodes, in the context of performance feedback comparing their performance on each episode with their respective goal. The proposed model was tested through a longitudinal study of 493 university students’ actual goals and performance on business school exams. Results of a structural equation model supported the proposed conceptual model in which self-efficacy and emotional reactions to feedback mediate the relationship between feedback and subsequent goals. In addition, as expected, participants’ standing on a dispositional measure of behavioral inhibition influenced the strength of their emotional reactions to negative feedback.

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