The current state of emergency communication is dispatch-mediated (the messages from the scene are directed towards the responders and agencies through the dispatch agency). These messages are logged in electronic documents called incident reports, which are useful in monitoring the incident, off-site supervision, resource allocation, and post-incident analysis. However, these messages do not adhere to any particular structure, and there is no set format. The lack of standards creates a problem for sharing information among systems and responders and has a detrimental impact on systems interoperability. In this article, we develop a National Information Exchange Model (NIEM) and Universal Core (UCORE) compliant messaging model, considering message structures and formats, to foster message standardization.
{"title":"A Dispatch-Mediated Communication Model for Emergency Response Systems","authors":"Rohit Valecha, R. Sharman, H. Rao, S. Upadhyaya","doi":"10.1145/2445560.2445562","DOIUrl":"https://doi.org/10.1145/2445560.2445562","url":null,"abstract":"The current state of emergency communication is dispatch-mediated (the messages from the scene are directed towards the responders and agencies through the dispatch agency). These messages are logged in electronic documents called incident reports, which are useful in monitoring the incident, off-site supervision, resource allocation, and post-incident analysis. However, these messages do not adhere to any particular structure, and there is no set format. The lack of standards creates a problem for sharing information among systems and responders and has a detrimental impact on systems interoperability. In this article, we develop a National Information Exchange Model (NIEM) and Universal Core (UCORE) compliant messaging model, considering message structures and formats, to foster message standardization.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127959335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bin Zhang, Andrew C. Thomas, P. Doreian, D. Krackhardt, R. Krishnan
The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for closeness that are appropriate, either through different types of friendships, or different functions of distance on one kind of friendship, where only a subset of these networks may actually be relevant. Another is that these decisions are often binary and more difficult to model with conventional approaches, both conceptually and computationally. To address these issues, we present a hierarchical auto-probit model for individual binary outcomes that uses and extends the machinery of the auto-probit method for binary data. We demonstrate the behavior of the parameters estimated by the multiple network-regime auto-probit model (m-NAP) under various sensitivity conditions, such as the impact of the prior distribution and the nature of the structure of the network. We also demonstrate several examples of correlated binary data outcomes in networks of interest to information systems, including the adoption of caller ring-back tones, whose use is governed by direct connection but explained by additional network topologies.
{"title":"Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption","authors":"Bin Zhang, Andrew C. Thomas, P. Doreian, D. Krackhardt, R. Krishnan","doi":"10.1145/2407740.2407742","DOIUrl":"https://doi.org/10.1145/2407740.2407742","url":null,"abstract":"The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for closeness that are appropriate, either through different types of friendships, or different functions of distance on one kind of friendship, where only a subset of these networks may actually be relevant. Another is that these decisions are often binary and more difficult to model with conventional approaches, both conceptually and computationally. To address these issues, we present a hierarchical auto-probit model for individual binary outcomes that uses and extends the machinery of the auto-probit method for binary data. We demonstrate the behavior of the parameters estimated by the multiple network-regime auto-probit model (m-NAP) under various sensitivity conditions, such as the impact of the prior distribution and the nature of the structure of the network. We also demonstrate several examples of correlated binary data outcomes in networks of interest to information systems, including the adoption of caller ring-back tones, whose use is governed by direct connection but explained by additional network topologies.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114895048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.
{"title":"Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline","authors":"R. Chiang, Paulo B. Góes, E. Stohr","doi":"10.1145/2361256.2361257","DOIUrl":"https://doi.org/10.1145/2361256.2361257","url":null,"abstract":"“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116054100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article describes an innovative approach to reorganizing the tag space generated by social bookmarking services. The objective of this work is to enable effective search and discovery of Web content using social bookmarking tags. Tags are metadata generated by users for Web content annotation. Their potential as effective Web search and discovery tool is hindered by challenges such as, the tag space being untidy due to ambiguity, and hidden or implicit semantics. Using a novel analytics approach, we conducted network analyses on tags and discovered that tags are generated for different purposes and that there are inherent relationships among tags. Our approach can be used to extract the purposes of tags and relationships among the tags and this information can be used as facets to add structure and hierarchy to reorganize the flat tag space. The semantics of relationships and hierarchy in our proposed faceted model of tags enable searches on annotated Web content in an effective manner. We describe the implementation of a prototype system called FASTS to demonstrate feasibility and effectiveness of our approach.
{"title":"Using a Network Analysis Approach for Organizing Social Bookmarking Tags and Enabling Web Content Discovery","authors":"Wei Wei, S. Ram","doi":"10.1145/2361256.2361260","DOIUrl":"https://doi.org/10.1145/2361256.2361260","url":null,"abstract":"This article describes an innovative approach to reorganizing the tag space generated by social bookmarking services. The objective of this work is to enable effective search and discovery of Web content using social bookmarking tags. Tags are metadata generated by users for Web content annotation. Their potential as effective Web search and discovery tool is hindered by challenges such as, the tag space being untidy due to ambiguity, and hidden or implicit semantics. Using a novel analytics approach, we conducted network analyses on tags and discovered that tags are generated for different purposes and that there are inherent relationships among tags. Our approach can be used to extract the purposes of tags and relationships among the tags and this information can be used as facets to add structure and hierarchy to reorganize the flat tag space. The semantics of relationships and hierarchy in our proposed faceted model of tags enable searches on annotated Web content in an effective manner. We describe the implementation of a prototype system called FASTS to demonstrate feasibility and effectiveness of our approach.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123175379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
By using online retail data collected from Amazon, Barnes & Nobel, and Pricegrabber, this paper investigates whether online vendors’’ pricing decisions fully reflect the information contained in various components of customers’ online reviews. The findings suggest that there is inefficiency in vendors’ pricing decisions. Specifically, vendors do not appear to fully understand the incremental predictive power of online reviews in forecasting future sales when they adjust their prices. However, they do understand demand persistence. Interestingly, vendors reduce price if the actual demand is higher than the expected demand (positive demand shock). This phenomenon is attributed to the advertising effect suggested in previous literature and the intense competitiveness of e-Commerce. Finally, we document that vendors do not change their prices directly in response to online reviews; their response to online reviews is through forecasting consumer’s future demand.
{"title":"Do Vendors’ Pricing Decisions Fully Reflect Information in Online Reviews?","authors":"Nan Hu, H. Cavusoglu, Lingbing Liu, Chenkai Ni","doi":"10.1145/2361256.2361261","DOIUrl":"https://doi.org/10.1145/2361256.2361261","url":null,"abstract":"By using online retail data collected from Amazon, Barnes & Nobel, and Pricegrabber, this paper investigates whether online vendors’’ pricing decisions fully reflect the information contained in various components of customers’ online reviews. The findings suggest that there is inefficiency in vendors’ pricing decisions. Specifically, vendors do not appear to fully understand the incremental predictive power of online reviews in forecasting future sales when they adjust their prices. However, they do understand demand persistence. Interestingly, vendors reduce price if the actual demand is higher than the expected demand (positive demand shock). This phenomenon is attributed to the advertising effect suggested in previous literature and the intense competitiveness of e-Commerce. Finally, we document that vendors do not change their prices directly in response to online reviews; their response to online reviews is through forecasting consumer’s future demand.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133444043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Real-time microblogging systems such as Twitter offer users an easy and lightweight means to exchange information. Instead of writing formal and lengthy messages, microbloggers prefer to frequently broadcast several short messages to be read by other users. Only when messages are interesting, are they propagated further by the readers. In this article, we examine user behavior relevant to information propagation through microblogging. We specifically use retweeting activities among Twitter users to define and model originating and promoting behavior. We propose a basic model for measuring the two behaviors, a mutual dependency model, which considers the mutual relationships between the two behaviors, and a range-based model, which considers the depth and reach of users’ original tweets. Next, we compare the three behavior models and contrast them with the existing work on modeling influential Twitter users. Last, to demonstrate their applicability, we further employ the behavior models to detect interesting events from sudden changes in aggregated information propagation behavior of Twitter users. The results will show that the proposed behavior models can be effectively applied to detect interesting events in the Twitter stream, compared to the baseline tweet-based approaches.
{"title":"Who is Retweeting the Tweeters? Modeling, Originating, and Promoting Behaviors in the Twitter Network","authors":"Palakorn Achananuparp, Ee-Peng Lim, Jing Jiang, Tuan-Anh Hoang","doi":"10.1145/2361256.2361258","DOIUrl":"https://doi.org/10.1145/2361256.2361258","url":null,"abstract":"Real-time microblogging systems such as Twitter offer users an easy and lightweight means to exchange information. Instead of writing formal and lengthy messages, microbloggers prefer to frequently broadcast several short messages to be read by other users. Only when messages are interesting, are they propagated further by the readers. In this article, we examine user behavior relevant to information propagation through microblogging. We specifically use retweeting activities among Twitter users to define and model originating and promoting behavior. We propose a basic model for measuring the two behaviors, a mutual dependency model, which considers the mutual relationships between the two behaviors, and a range-based model, which considers the depth and reach of users’ original tweets. Next, we compare the three behavior models and contrast them with the existing work on modeling influential Twitter users. Last, to demonstrate their applicability, we further employ the behavior models to detect interesting events from sudden changes in aggregated information propagation behavior of Twitter users. The results will show that the proposed behavior models can be effectively applied to detect interesting events in the Twitter stream, compared to the baseline tweet-based approaches.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116897274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hsin-Min Lu, Feng-Tse Tsai, Hsinchun Chen, Mao-Wei Hung, Shu-Hsing Li
Credit ratings convey credit risk information to participants in financial markets, including investors, issuers, intermediaries, and regulators. Accurate credit rating information plays a crucial role in supporting sound financial decision-making processes. Most previous studies on credit rating modeling are based on accounting and market information. Text data are largely ignored despite the potential benefit of conveying timely information regarding a firm’s outlook. To leverage the additional information in news full-text for credit rating prediction, we designed and implemented a news full-text analysis system that provides firm-level coverage, topic, and sentiment variables. The novel topic-specific sentiment variables contain a large fraction of missing values because of uneven news coverage. The missing value problem creates a new challenge for credit rating prediction approaches. We address this issue by developing a missing-tolerant multinomial probit (MT-MNP) model, which imputes missing values based on the Bayesian theoretical framework. Our experiments using seven and a half years of real-world credit ratings and news full-text data show that (1) the overall news coverage can explain future credit rating changes while the aggregated news sentiment cannot; (2) topic-specific news coverage and sentiment have statistically significant impact on future credit rating changes; (3) topic-specific negative sentiment has a more salient impact on future credit rating changes compared to topic-specific positive sentiment; (4) MT-MNP performs better in predicting future credit rating changes compared to support vector machines (SVM). The performance gap as measured by macroaveraging F-measure is small but consistent.
{"title":"Credit Rating Change Modeling Using News and Financial Ratios","authors":"Hsin-Min Lu, Feng-Tse Tsai, Hsinchun Chen, Mao-Wei Hung, Shu-Hsing Li","doi":"10.1145/2361256.2361259","DOIUrl":"https://doi.org/10.1145/2361256.2361259","url":null,"abstract":"Credit ratings convey credit risk information to participants in financial markets, including investors, issuers, intermediaries, and regulators. Accurate credit rating information plays a crucial role in supporting sound financial decision-making processes. Most previous studies on credit rating modeling are based on accounting and market information. Text data are largely ignored despite the potential benefit of conveying timely information regarding a firm’s outlook. To leverage the additional information in news full-text for credit rating prediction, we designed and implemented a news full-text analysis system that provides firm-level coverage, topic, and sentiment variables. The novel topic-specific sentiment variables contain a large fraction of missing values because of uneven news coverage. The missing value problem creates a new challenge for credit rating prediction approaches. We address this issue by developing a missing-tolerant multinomial probit (MT-MNP) model, which imputes missing values based on the Bayesian theoretical framework. Our experiments using seven and a half years of real-world credit ratings and news full-text data show that (1) the overall news coverage can explain future credit rating changes while the aggregated news sentiment cannot; (2) topic-specific news coverage and sentiment have statistically significant impact on future credit rating changes; (3) topic-specific negative sentiment has a more salient impact on future credit rating changes compared to topic-specific positive sentiment; (4) MT-MNP performs better in predicting future credit rating changes compared to support vector machines (SVM). The performance gap as measured by macroaveraging F-measure is small but consistent.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122728045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Within the emerging context of Web 2.0 social media, online customer reviews are playing an increasingly important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. The sheer volume of customer reviews on the web produces information overload for readers. Developing a system that can automatically identify the most helpful reviews would be valuable to businesses that are interested in gathering informative and meaningful customer feedback. Because the target variable---review helpfulness---is continuous, common feature selection techniques from text classification cannot be applied. In this article, we propose and investigate a text mining model, enhanced using the Regressional ReliefF (RReliefF) feature selection method, for predicting the helpfulness of online reviews from Amazon.com. We find that RReliefF significantly outperforms two popular dimension reduction methods. This study is the first to investigate and compare different dimension reduction techniques in the context of applying text regression for predicting online review helpfulness. Another contribution is that our analysis of the keywords selected by RReliefF reveals meaningful feature groupings.
{"title":"Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method","authors":"Thomas L. Ngo-Ye, Atish P. Sinha","doi":"10.1145/2229156.2229158","DOIUrl":"https://doi.org/10.1145/2229156.2229158","url":null,"abstract":"Within the emerging context of Web 2.0 social media, online customer reviews are playing an increasingly important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. The sheer volume of customer reviews on the web produces information overload for readers. Developing a system that can automatically identify the most helpful reviews would be valuable to businesses that are interested in gathering informative and meaningful customer feedback. Because the target variable---review helpfulness---is continuous, common feature selection techniques from text classification cannot be applied. In this article, we propose and investigate a text mining model, enhanced using the Regressional ReliefF (RReliefF) feature selection method, for predicting the helpfulness of online reviews from Amazon.com. We find that RReliefF significantly outperforms two popular dimension reduction methods. This study is the first to investigate and compare different dimension reduction techniques in the context of applying text regression for predicting online review helpfulness. Another contribution is that our analysis of the keywords selected by RReliefF reveals meaningful feature groupings.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130209637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A key issue in process-aware e-commerce collaboration is to orchestrate business processes of multiple business partners throughout a supply chain network in an automated and seamless way. Since each partner has its own internal processes with different control flow structures and message interfaces, the real challenge lies in verifying the correctness of process collaboration, and reconciling conflicts in an automated manner to make collaboration successful. The purpose of business process adaptation is to mediate the communication between independent processes to overcome their mismatches and incompatibilities. The goal of this article is to develop and compare efficient approaches of optimal adapter (i.e. one that minimizes the number of messages to be adapted) creation for multiple interacting processes under both synchronous and asynchronous communication. We start with an analysis of interactions of each message pair, and show how to identify incompatible cases and their adaptation elements for both types of communication. Then, we show how to extend this analysis into more general cases involving M messages and N processes (M, N > 2). Further, we present optimal adapter creation algorithms for both scenarios based on our analysis technique. The algorithms were implemented in a Java-based prototype system, and results of two experiments are reported. We compare and discuss the insights gained about adapter creation in these two scenarios.
流程感知电子商务协作中的一个关键问题是以自动化和无缝的方式在整个供应链网络中编排多个业务合作伙伴的业务流程。由于每个合作伙伴都有自己的内部流程，具有不同的控制流结构和消息接口，因此真正的挑战在于验证流程协作的正确性，并以自动化的方式协调冲突以使协作成功。业务流程适应的目的是调解独立流程之间的通信，以克服它们的不匹配和不兼容。本文的目标是开发和比较为同步和异步通信下的多个交互流程创建最佳适配器(即最小化要适应的消息数量)的有效方法。我们首先分析每个消息对的交互，并展示如何识别不兼容的情况及其对两种通信类型的适应元素。然后，我们展示了如何将此分析扩展到涉及M个消息和N个进程(M, N > 2)的更一般的情况。此外，我们基于我们的分析技术为这两种场景提供了最佳适配器创建算法。在基于java的原型系统中实现了该算法，并给出了两个实验结果。我们比较并讨论了在这两种场景中关于适配器创建的见解。
{"title":"Optimal Adapter Creation for Process Composition in Synchronous vs. Asynchronous Communication","authors":"Zhe Shan, Akhil Kumar","doi":"10.1145/2229156.2229160","DOIUrl":"https://doi.org/10.1145/2229156.2229160","url":null,"abstract":"A key issue in process-aware e-commerce collaboration is to orchestrate business processes of multiple business partners throughout a supply chain network in an automated and seamless way. Since each partner has its own internal processes with different control flow structures and message interfaces, the real challenge lies in verifying the correctness of process collaboration, and reconciling conflicts in an automated manner to make collaboration successful. The purpose of business process adaptation is to mediate the communication between independent processes to overcome their mismatches and incompatibilities. The goal of this article is to develop and compare efficient approaches of optimal adapter (i.e. one that minimizes the number of messages to be adapted) creation for multiple interacting processes under both synchronous and asynchronous communication. We start with an analysis of interactions of each message pair, and show how to identify incompatible cases and their adaptation elements for both types of communication. Then, we show how to extend this analysis into more general cases involving M messages and N processes (M, N > 2). Further, we present optimal adapter creation algorithms for both scenarios based on our analysis technique. The algorithms were implemented in a Java-based prototype system, and results of two experiments are reported. We compare and discuss the insights gained about adapter creation in these two scenarios.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125261111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ability to predict consumer choices is essential in understanding the demand structure of products and services. Typical discrete choice models that are targeted at providing an understanding of the behavioral process leading to choice outcomes are developed around two main assumptions: the existence of a utility function that represents the preferences over a choice set and the relatively simple and interpretable functional form for the utility function with respect to attributes of alternatives and decision makers. These assumptions lead to models that can be easily interpreted to provide insights into the effects of individual variables, such as price and promotion, on consumer choices. However, these restrictive assumptions might impede the ability of such theory-driven models to deliver accurate predictions and forecasts. In this article, we develop novel approaches targeted at providing more accurate choice predictions. Specifically, we propose two prediction-driven approaches: pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We compare our proposed approaches with a multiclass classification approach, as well as a standard discrete choice model. Our empirical results show that the proposed approaches achieved significantly higher choice prediction accuracy.
{"title":"Two New Prediction-Driven Approaches to Discrete Choice Prediction","authors":"Zan Huang, Huimin Zhao, Dan Zhu","doi":"10.1145/2229156.2229159","DOIUrl":"https://doi.org/10.1145/2229156.2229159","url":null,"abstract":"The ability to predict consumer choices is essential in understanding the demand structure of products and services. Typical discrete choice models that are targeted at providing an understanding of the behavioral process leading to choice outcomes are developed around two main assumptions: the existence of a utility function that represents the preferences over a choice set and the relatively simple and interpretable functional form for the utility function with respect to attributes of alternatives and decision makers. These assumptions lead to models that can be easily interpreted to provide insights into the effects of individual variables, such as price and promotion, on consumer choices. However, these restrictive assumptions might impede the ability of such theory-driven models to deliver accurate predictions and forecasts. In this article, we develop novel approaches targeted at providing more accurate choice predictions. Specifically, we propose two prediction-driven approaches: pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We compare our proposed approaches with a multiclass classification approach, as well as a standard discrete choice model. Our empirical results show that the proposed approaches achieved significantly higher choice prediction accuracy.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120954043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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