Maximal Mixed-Drove Co-occurrence Patterns

2023 | Information Systems Frontiers | Citations: 0

Authors: Andrzejewski, Witold; Boinski, Pawel

Abstract: One of the interesting types of spatio-temporal patterns is the Mixed-Drove Co-o ... Expand

Abstract: One of the interesting types of spatio-temporal patterns is the Mixed-Drove Co-occurrence Pattern (MDCOP), which represents types of objects frequently located together in both space and time. To discover such patterns, methods based on the well-known Apriori strategy are widely used. They involve determining multi-element MDCOPs by building them up iteratively starting with the two-element patterns and then successively adding another element in each iteration. This approach can be very costly, particularly when the data is dense enough to form patterns of significant size. In this paper, we introduce a definition of a new pattern type called a Maximal Mixed-Drove Co-occurrence Pattern. We also propose a new algorithm MAXMDCOP-Miner, which resigns from popular Apriori strategy of generating candidates and, therefore, can discover long pattern without processing all their subsets. Experiments performed on synthetic and real datasets show that MAXMDCOP-Miner has high performance, in particular for dense datasets or tasks with low user-defined thresholds of spatial or time prevalence. Collapse

Methods: computational algorithm experiment machine learning literature study association rule learning

Understanding active participation of online dating services: A mixed methods study

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Qi; Yun, Guobiao; Lu, Jiandong; Wang, Xiaolei

Abstract: Online dating has transformed the way people meet and start relationships. Howev ... Expand

Abstract: Online dating has transformed the way people meet and start relationships. However, online dating platforms typically have many lurkers without active participation. Using a self-regulation framework, this paper aims to study users’ active participation in online dating through a mixed methods study. We first conducted qualitative and quantitative investigations with a machine learning method to explore user active participation and its antecedents. We then developed a theoretical model and conducted a statistical analysis of the model. The empirical findings showed that service quality and trust significantly affected individual active participation in a dating site through attachments to the daters and services. The findings of the study contribute to the extant literature by analyzing user participation in online dating from an attachment perspective. Collapse

Topics: service quality online dating participatory design online dating service Python

Methods: design methodology mixed method qualitative coding machine learning machine learning method

Theories: attachment theory marketing theory

Smart System: Joint Utility and Frequency for Pattern Classification

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Lin, Qi; Gan, Wensheng; Wu, Yongdong; Chen, Jiahui; Chen, Chien-Ming

Abstract: Nowadays, the environments of smart systems for Industry 4.0 and Internet of Thi ... Expand

Abstract: Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized, which will help manufacturing organizations to finish another round of upgrading. In this article, we propose two new algorithms with respect to big data analysis, namely UFCgen and UFCfast. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFCfast algorithm outperforms the levelwise-based UFCgen algorithm in terms of both execution time and memory consumption. Collapse

Topics: database system production management smart manufacturing operating system Microsoft Windows

Methods: computational algorithm experiment case study machine learning literature study

Domain-independent real-time service provisioning in digital platforms: Featuring bundling and customer time-preference

2023 | Decision Support Systems | Citations: 0

Authors: Mukherjee, Anik; Rangaraja P., Sundarraj; Vander Meer, Debra; Dutta, Kaushik

Abstract: Digital platforms have emerged as an important technology underpinning the new e ... Expand

Abstract: Digital platforms have emerged as an important technology underpinning the new economy. A key problem in such platforms concerns provisioning decisions for customer-service requests, in order to maximize the pro­ vider’s revenue subject to resource availability. Provisioning is important to meet customer needs, and in turn, for customer retention. The service provisioning problem, in addition to being NP-hard, is further compounded by the fact that the timeframe to make provisioning decisions is short, and more importantly, varies considerably across different platform domains. Digital-platform customers often request bundled services and are sensitive to when tasks get completed. We describe a behavioral-economic model of this problem and develop a domainindependent solution featuring a judicious combination of optimization-backed rule-based approach. Through extensive computational results, we show how our approach has the potential for generalizability and tailor­ ability across different domains in a seamless manner, thus offering the advantages proposed in the softwarereusability literature. Implications to platform sustainability, reusable platform solutions and to practical ap­ proaches to real-time platforms are also provided. Collapse

Topics: digital platform decision making cloud computing ride sharing platform smartphone

Methods: experiment computational algorithm knapsack problem association rule learning simulation

Theories: behavioral economics

#MyIBDHistory on Twitter: Identifying Disease Characteristics Using Personal Tweets

2023 | HICSS | Citations: 0

Authors: Stemmer, Maya; Ravid, Gilad; Parmet, Yisrael

Abstract: Inflammatory bowel disease (IBD) is usually classified into Crohn's disease (CD) ... Expand

Abstract: Inflammatory bowel disease (IBD) is usually classified into Crohn's disease (CD) or ulcerative colitis (UC). Inconclusive cases are diagnosed with IBD unclassified (IBD-U). In 2018, IBD patients shared their disease history on Twitter and signed their tweets with #MyIBDHistory. In this research, we analyzed those tweets and built a logistic regression classifier that predicts patients' IBD type. We constructed tabular classification features and assessed their importance using the regression coefficients and association rules. We identified key features that distinguished CD from UC and used the classifier to predict the disease type of IBD-U patients. Our results correlated with IBD-related research. The two most prominent features that tilted the classification towards CD were suffering from fistulas or nutrient deficiencies. We identified gender differences in disease perspective prior to diagnosis. The research shows that the personal information shared by patients on Twitter can enhance existing medical knowledge regarding their disease. Collapse

Topics: Twitter database system logistics management privacy application programming interface

Methods: logistic regression lasso regression association rule learning regression analysis method descriptive statistic

Factors Affecting Customer Analytics: Evidence from Three Retail Cases

2022 | Information Systems Frontiers | Citations: 0

Authors: Griva, Anastasia; Bardaki, Cleopatra; Pramatari, Katerina; Doukidis, Georgios

Abstract: The abundance of customer behavioral data alters the design and application of c ... Expand

Abstract: The abundance of customer behavioral data alters the design and application of customer analytics systems and approaches. Segmentation is a common customer analytics practice, but researchers highlight that traditional segmentation approaches are not enough. We coin the term “visit segmentation” and devise a visit segmentation approach. When designing or applying a new information system or approach, it is important to consider factors related to the input data, the application context, the users, and all the relevant requirements. Considering the literature, this paper identifies such factors that affect customer analytics approaches and systems. We explore how these factors affect segmentation through applying our segmentation approach to three heterogeneous retailers, e.g., the products’ variety a shopper purchases in each visit seems to be crucial to the segmentation. The more attention data analysts and designers pay to these factors, the more reliable segmentation results they will get and, thus, improved retail decisions are expected. Collapse

Topics: electronic commerce marketing management analytical information system decision making structured query language

Methods: cluster analysis design science descriptive statistic computational algorithm k-means clustering

The art of persuasion: An integrated text mining model for crowdfunding analysis

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Hong, Sukhwa; Goldberg, David

Abstract: Crowdfunding is a substantial new funding source and growing in popularity, pro ... Expand

Abstract: Crowdfunding is a substantial new funding source and growing in popularity, providing a vital service to individuals especially in developing countries, who could not otherwise get the financial resources they need. Understanding factors that determine the funders' decisions is important in writing persuasive crowdfunding pitches for successful projects. In this paper, we present an integrated text-mining model that combines the bag-of-words model and related variables for predicting the success of crowdfunding projects in Kiva. Furthermore, association rule mining is used to identify factors that influence the funders' decisions for writing persuasive crowdfunding pitches for successful projects. Collapse

Topics: crowdfunding crowdsourcing website

Methods: association rule learning latent dirichlet allocation classification method natural language processing machine learning

Pattern Labelling of Business Communication Data

2022 | Group Decision and Negotiation | Citations: 0

Authors: Kaya, Muhammed-Fatih

Abstract: Systematic pattern recognition as well as the corresponding description of deter ... Expand

Abstract: Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the determination of regularities in underlying datasets. Due to the increased expansion of dimensions in multidimensional data spaces, determined patterns are no longer interpretable by humans. Taking these challenges into account, this paper investigates to what extent pre-defined communication patterns can be interpreted for the application area of high-dimensional business communication data. An analytical perspective is considered by taking into account a holistic research approach and by subsequently applying selected Machine Learning methods from Association Rule Discovery, Topic Modelling and Decision Trees with regard to the overall goal of semi-automated pattern labelling. The results show that meaningful descriptions can be derived for the interpretation of pre-defined patterns. Collapse

Topics: cloud computing database system negotiation support system business-to-business

Methods: data transformation principal component analysis DBSCAN decision tree classification computational algorithm

From free to fee: Monetizing digital content through expected utility-based recommender systems

2022 | Information & Management | Citations: 0

Authors: Lee, Dongwon; Nam, Kihwan; Han, Ingoo; Cho, Kanghyun

Abstract: This study proposes a novel framework for designing business rule analytics to a ... Expand

Abstract: This study proposes a novel framework for designing business rule analytics to assist businesses offering digital content in effectively converting free-only users (FOUs) into paying customers. Based on the theory of expected utility, we expand upon traditional frequency-driven rule analytics by integrating three business-relevant factors (target size, conversion profit, and conversion likelihood) into the process of generating recommendations for FOUs in digital content markets. The framework was tested using two different types of empirical analysis. We conducted a field experiment collaborating with a nationwide e-book store to determine how FOUs responded to the recommendations generated under the proposed framework. Furthermore, we analyzed over 5 million transactions collected from the e-book seller and a mobile application provider to examine the impact of customer segmentation on the effectiveness of our approach. Our findings suggest that business analytics derived from the utility-based mechanisms can significantly enhance digital content providers’ business performance. Collapse

Topics: ebook digital good mobile application recommender system analytical information system

Methods: experiment archival research field experiment descriptive statistic Bayesian analysis

Complement or substitute? Investigating the interdependence effects among mobile social apps

2022 | Information & Management | Citations: 0

Authors: Wang, Chuang; Zheng, Shaochun

Abstract: Interdependence effects would arise when individuals consume two or more product ... Expand

Abstract: Interdependence effects would arise when individuals consume two or more products simultaneously. However, comprehensive understanding and quantitative analysis of such interdependence effects in the use of mobile social apps are still limited. The few existing studies mainly considered whether the apps are installed together by unique devices, while the actual usage behaviors are totally ignored. The current study developed an adapted utility function model to quantify the interdependence effects and found that social apps satisfying users’ different psychological needs would be complementary at most times, while those apps gratifying similar psy­ chological needs would also complement rather than substitute each other. Collapse

Topics: mobile application WeChat Tencent QQ Sina Weibo information system use

Methods: longitudinal research usage data econometric modeling computational algorithm economic model

Theories: theory of affordance theory of economic development