Data-driven User Research Articles

Application of Sentiment Analysis and Data-Driven User Profiling in Product Iteration Design

Our study employs sentiment analysis and data-driven methodologies to construct precise user profiles and apply them to product iteration design, aiming to enhance marketing effectiveness and user satisfaction. The research data comprises 178,563 user comments and 42,786 social media posts. Sentiment features are extracted using advanced sentiment analysis algorithms such as BERT and Transformer. These features are combined with user behavior data and classified into five primary groups using the K-means clustering algorithm, each representing distinct user needs and sentiment tendencies. Based on the user profiles, three product iteration schemes were designed and implemented. The effectiveness of these iterations was validated through A/B testing, resulting in significant improvements in user satisfaction and usage rates. Regression analysis reveals that both sentiment scores and user behavior features have a significant positive impact on user satisfaction. The findings demonstrate that combining sentiment analysis with data-driven user profiling plays a crucial role in product design and marketing, offering new insights for optimizing products and enhancing competitiveness.

Data-driven strategies for enhancing user engagement in digital platforms

User engagement is a critical metric for success in digital platforms, influencing everything from customer retention to revenue generation. This paper explores data-driven strategies aimed at enhancing user engagement. By leveraging insights from user data, businesses can better understand user behavior, personalize experiences, and optimize interactions. Techniques such as A/B testing, gamification, and social proof can be employed to drive engagement and foster a sense of community. Feedback mechanisms enable continuous improvement, while predictive analytics empower proactive engagement strategies. However, it's essential to prioritize data privacy and ethical considerations throughout these endeavors. Through case studies and best practices, this paper highlights successful implementations and provides insights into future trends in data-driven user engagement. Keywords: User Engagement, Digital Platforms, Data-Driven Strategies, Predictive Analytics, Data Privacy.

Big Data-driven Decision Support: Enhancing Information Integration and User Experience with Mobile Integrated Technology

This study examines how big data-driven decision support and mobile technology interact to improve information integration and user experience. The research studies big data for digital decision-making and provides theoretical and practical suggestions to assist organizations in overcoming its challenges. This study used mixed method analysis to find the relationship between big data-driven user experience and mobile-integrated technology. Businesses require sophisticated decision support tools to navigate the digital landscape of massive data. Big data-driven decision support is examined to determine how information integration and user experience affect mobile-integrated technologies. A rigorous quantitative technique examines data volume and decision precision. Although big data volumes may have diminishing returns, decision-making generally improves. The study emphasizes the delicate balance between data volume, quality, velocity, diversity, and governance. Beyond quantitative analysis, the study examines complex decision-making. Information integration methods and user experience affect decision-making time, with more data offering strategic options. Agile integration and user-centric design boost efficiency and decision-making. The research highlights the change in mobile integrated technology. The title fits the research since mobile technology increases information integration and user experience. According to the study, mobile technology's user-friendly gadgets, quick internet connectivity, security safeguards, and app functionality boost user contentment, productivity, and decision-making accuracy. The report also emphasizes big data governance in decision quality. Decision support systems need big data governance for data access, accuracy, security, and compliance. Finally, this study provides theoretical insights into big data-driven decision support and practical suggestions for organizations navigating it. The study uses data, technology, user experience, and governance to improve business decision-making. This provides them with digital-era precision, agility, and strategic edge.

Dynamic user profile construction and its application to smart product-service system design: A maternity-oriented case study

The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate, and actionable framework and methodology to express the human element, namely user profiling, within this philosophy. To address this gap, this paper utilizes statistical methods such as factor analysis, cluster analysis, and regression analysis, building upon the concept of traditional user profiling. The aim is to integrate the three prominent approaches of goal-oriented, scenario-based, and data-driven user profiling, with the goal of complementing each other and designing a top-level user profiling framework for smart PSS. Furthermore, Industry 4.0 technologies and text mining techniques are employed to collect data on users' product and service usage. As these data contain real-time information about user needs, behaviors and goals at different time periods, they can be used to construct dynamic features of user profiling, and ultimately achieve the construction of dynamic user profiling for smart PSS. To validate the proposed user profiling framework and dynamic user features of smart PSS, this paper presents a case study focusing on the user group of maternal women. This case study promotes the in-depth exploration of smart PSS research in expressing the human element.

IoT in Home Automation: A Data-Driven User Behaviour Analysis and User Adoption Test

This research carried out a thorough data-driven examination of user behaviour, adoption rates, satisfaction, and energy efficiency in the context of IoT in home automation, within the quickly changing environment of smart homes and Internet of Things (IoT) technologies. The study found that users interacted with various kinds of IoT devices in diverse ways. Smart security systems and thermostats, for example, were quickly adopted and received high levels of satisfaction. The potential for significant energy savings demonstrated the contribution of IoT devices to sustainability. These results highlight the significance of making well-informed decisions when using IoT technology to create smarter, more efficient, and greener living environments. They also provide useful insights for manufacturers, legislators, and homeowners.

Toward Artificial Empathy for Human-Centered Design

Abstract In the early stages of the design process, designers explore opportunities by discovering unmet needs and developing innovative concepts as potential solutions. From a human-centered design perspective, designers must develop empathy with people to truly understand their experiences and needs. However, developing empathy is a complex and subjective process that relies heavily on the designer's empathic capability, and is often subject to the experiences of a small group of people. Therefore, the development of empathic understanding is intuitive, and the discovery of underlying needs can be serendipitous and unrepresentative. This paper aims to provide insights from artificial intelligence research to indicate the future direction of AI-driven human-centered design, considering the essential role of empathy. Specifically, we conduct an interdisciplinary investigation of research areas such as data-driven user research, empathic design, and artificial empathy. Based on this foundation, we discuss the role that artificial empathy can play in human-centered design and propose an artificial empathy framework for human-centered design. Building on the mechanisms behind empathy and insights from empathic design research, the framework aims to break down the rather complex and subjective process of developing empathic understanding into modules and components that can potentially be modeled computationally. Furthermore, we discuss the expected benefits of developing such systems and identify research opportunities to suggest future research efforts.

Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation

Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USM-based IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view optimization for personalized recommendation. Firstly, multidimensional preference user surrogate models (MPUSMs), partial-MPUSMs, and probability models of MPUSMs are constructed to approximate the different perceptions of preferences and serve for population evolution. Next, a modified multifactorial evolutionary algorithm is used for the first time in the IEAs domain to recommend diverse and novel items for multiple preferences. It includes initialization and diversification management of a population with skill factors, recommendation lists of preference grading and interactive model management of inheriting previous information. Comprehensive comparison studies in the Amazon dataset show that the proposed models and algorithm facilitate the mining of knowledge between preferences. Eventually, at the cost of losing only about 5% of the Hit Ratio and Average Precision, the Individual Diversity is improved by 54.02%, the Self-system Diversity by 3.7%, the Surprise Degree by 2.69%, and the Preference Mining Degree by 16.05%.

Big Data, Small Personas: How Algorithms Shape the Demographic Representation of Data-Driven User Segments.

Derived from the notion of algorithmic bias, it is possible that creating user segments such as personas from data results in over- or under-representing certain segments (FAIRNESS), does not properly represent the diversity of the user populations (DIVERSITY), or produces inconsistent results when hyperparameters are changed (CONSISTENCY). Collecting user data on 363M video views from a global news and media organization, we compare personas created from this data using different algorithms. Results indicate that the algorithms fall into two groups: those that generate personas with low diversity-high fairness and those that generate personas with high diversity-low fairness. The algorithms that rank high on diversity tend to rank low on fairness (Spearman's correlation: -0.83). The algorithm that best balances diversity, fairness, and consistency is Spectral Embedding. The results imply that the choice of algorithm is a crucial step in data-driven user segmentation, because the algorithm fundamentally impacts the demographic attributes of the generated personas and thus influences how decision makers view the user population. The results have implications for algorithmic bias in user segmentation and creating user segments that not only consider commercial segmentation criteria but also consider criteria derived from ethical discussions in the computing community.

Survey on Technique and User Profiling in Unsupervised Machine Learning Method

In order to generate precise behavioural patterns or user segmentation, organisations often struggle with pulling information from data and choosing suitable Machine Learning (ML) techniques. Furthermore, many marketing teams are unfamiliar with data-driven classification methods. The goal of this research is to provide a framework that outlines the Unsupervised Machine Learning (UML) methods for User-Profiling (UP) based on essential data attributes. A thorough literature study was undertaken on the most popular UML techniques and their dataset attributes needs. For UP, a structure is developed that outlines several UML techniques. In terms of data size and dimensions, it offers two-stage clustering algorithms for category, quantitative, and mixed types of datasets. The clusters are determined in the first step using a multilevel or model-based classification method. Cluster refining is done in the second step using a non-hierarchical clustering technique. Academics and professionals may use the framework to figure out which UML techniques are best for creating strong profiles or data-driven user segmentation.

Practical Method for Mitigating Three-Phase Unbalance Based on Data-Driven User Phase Identification

Unbalanced operation of a three-phase distribution system could incur more power losses, compared with the balanced operation. So far, the phase information of users in low voltage areas is not recorded by power utilities and the three-phase unbalance degree is very significant in some low voltage distribution systems. Given this background, this letter presents an integrated method for solving related issues including user phase identification based on spectral clustering and three-phase unbalance mitigation, and a Mixed Integer Linear Programming (MILP) model is then formulated. Some actual cases in Zhejiang province, China, are utilized to verify the effectiveness of the presented phase identification algorithm, and simulation results show that the three-phase unbalance can be significantly mitigated.

Data-driven user behavioral modeling: from real-world behavior to knowledge, algorithms, and systems

Data-driven user behavioral modeling: from real-world behavior to knowledge, algorithms, and systems

When Simpler Data Does Not Imply Less Information

The exponential growth in smartphone adoption is contributing to the availability of vast amounts of human behavioral data. This data enables the development of increasingly accurate data-driven user models that facilitate the delivery of personalized services that are often free in exchange for the use of its customers’ data. Although such usage conventions have raised many privacy concerns, the increasing value of personal data is motivating diverse entities to aggressively collect and exploit the data. In this article, we unfold profiling scenarios around mobile HTTP(S) traffic, focusing on those that have limited but meaningful segments of the data. The capability of the scenarios to profile personal information is examined with real user data, collected in the wild from 61 mobile phone users for a minimum of 30 days. Our study attempts to model heterogeneous user traits and interests, including personality, boredom proneness, demographics, and shopping interests. Based on our modeling results, we discuss various implications to personalization, privacy, and personal data rights.

Tweet Me What You Want, What You Really Really Want - The Informative Value of Tweets for Customer Feedback in Crowdfunding

Twitter allows users to publicly share their opinions on certain products in the form of micro-blogs. Such data can be collected and classified to help crowdfunding campaign owners to infer user needs, detect problems with their product and services and plan for future releases of their offers. However, automatically capturing, classifying, and presenting useful tweets is not a trivial task. Challenges stem from the scale of the data available, its unique format, its diverse nature, and the high percentage of irrelevant information and spam. Motivated by these challenges, this paper reports on a two-fold study that aims at leveraging Twitter as a main source of feedback on crowdfunding campaigns. The main objective is to enable a responsive, interactive, and adaptive data-driven user feedback process. Our analysis is conducted on a random sample of 3,600 tweets that we collected from Twitter feeds of 12 crowdfunding campaigns. Our preliminary results reveal that around 47% of collected tweets contain useful information that campaign owners can use for feedback. Moreover, the paper discusses effective classification- and summarization-techniques to analyze and present the information contained by tweets in an adequate manner.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

Hybrid user intention modeling to diversify dialog simulations

This paper proposes a novel user intention simulation method which is data-driven but can integrate diverse user discourse knowledge to simulate various types of user behaviors. A method of data-driven user intention modeling based on logistic regression is introduced in the Markov logic framework. Human dialog knowledge is designed into two layers, domain and discourse knowledge, and integrated with the data-driven model in generation time. Three types of user knowledge, i.e., cooperative, corrective and self-directing, are designed and integrated to generate behaviors of corresponding user-types. In experiments to investigate the patterns of simulated users, the approach successfully generated cooperative, corrective and self-directing user intention patterns.

Data-driven user simulation for automated evaluation of spoken dialog systems

This paper proposes a novel integrated dialog simulation technique for evaluating spoken dialog systems. A data-driven user simulation technique for simulating user intention and utterance is introduced. A novel user intention modeling and generating method is proposed that uses a linear-chain conditional random field, and a two-phase data-driven domain-specific user utterance simulation method and a linguistic knowledge-based ASR channel simulation method are also presented. Evaluation metrics are introduced to measure the quality of user simulation at intention and utterance. Experiments using these techniques were carried out to evaluate the performance and behavior of dialog systems designed for car navigation dialogs and a building guide robot, and it turned out that our approach was easy to set up and showed similar tendencies to real human users.

CFGP: a web-based, comparative fungal genomics platform

Since the completion of the Saccharomyces cerevisiae genome sequencing project in 1996, the genomes of over 80 fungal species have been sequenced or are currently being sequenced. Resulting data provide opportunities for studying and comparing fungal biology and evolution at the genome level. To support such studies, the Comparative Fungal Genomics Platform (CFGP; http://cfgp.snu.ac.kr), a web-based multifunctional informatics workbench, was developed. The CFGP comprises three layers, including the basal layer, middleware and the user interface. The data warehouse in the basal layer contains standardized genome sequences of 65 fungal species. The middleware processes queries via six analysis tools, including BLAST, ClustalW, InterProScan, SignalP 3.0, PSORT II and a newly developed tool named BLASTMatrix. The BLASTMatrix permits the identification and visualization of genes homologous to a query across multiple species. The Data-driven User Interface (DUI) of the CFGP was built on a new concept of pre-collecting data and post-executing analysis instead of the ‘fill-in-the-form-and-press-SUBMIT’ user interfaces utilized by most bioinformatics sites. A tool termed Favorite, which supports the management of encapsulated sequence data and provides a personalized data repository to users, is another novel feature in the DUI.

The human-computer dialogue management of FCAD system

This paper describes the dialogue management of FCAD’s user interface through introducing FCAD’s data-driven user interface model which eases the creation, modification, analyzing, and testing of it.

UMFE: A user modelling front-end subsystem

The paper argues that user models are an essential component of any system which attempts to be “user friendly”, and that expert systems should tailor explanations to their users, be they super-experts or novices. In particular, this paper discusses a data-driven user modelling front-end subsystem, UMFE, which assumes that the user has asked a question of the main system (e.g. an expert system, intelligent tutoring system etc.), and that the system provides a response which is passed to UMFE. UMFE determines the user's level of sophistication by asking as few questions as possible, and then presents a response in terms of concepts which UMFE believes the user understands. Investigator-defined inference rules are then used to suggest additional concepts the user may/may not know, given the concepts the user indicated he or she knew in earlier questioning. Several techniques are discussed for detecting and removing inconsistencies in the user model. Additionally, UMFE modifies its inference rules for individual users when it detects certain types of inconsistencies. UMFE is a portable domain-independent implementation of a system which infers overlay models for users. UMFE has been used in conjunction with NEOMYCIN; and the paper contains several protocols which demonstrate its principal features. The paper concludes with a critique of UMFE and suggestions for enhancing the current system.