2,230 research outputs found

    Towards Correlated Sequential Rules

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    The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or useful sequential patterns in a large number of sequences. However, simply being aware of utility-eligible patterns is insufficient for making predictions. To compensate for this deficiency, high-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns based on the appearance of premise sequential patterns. It has numerous applications, such as product recommendation and weather prediction. However, the existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules. To address this issue, we propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM. The proposed algorithm requires not only that each rule be correlated but also that the patterns in the antecedent and consequent of the high-utility sequential rule be correlated. The algorithm adopts a utility-list structure to avoid multiple database scans. Additionally, several pruning strategies are used to improve the algorithm's efficiency and performance. Based on several real-world datasets, subsequent experiments demonstrated that CoUSR is effective and efficient in terms of operation time and memory consumption.Comment: Preprint. 7 figures, 6 table

    Privacy Preserving Utility Mining: A Survey

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    In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

    UNDERSTANDING CONSUMERS' ONLINE INFORMATION RETRIEVAL AND SEARCH: IMPLICATIONS FOR FIRM STRATEGIES

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    The growth of the Internet and other digitization technologies has enabled the unbundling of the physical and information components of the value chain and has led to an explosion of information made available to consumers. Understanding the implications of this new informational landscape for theory and practice is one of the key objectives of my research. My dissertation seeks to understand how firms can use their knowledge of online consumer search and information seeking behaviors to design optimal information provision strategies. The main premise is that consumers' online search behaviors are key to understanding consumers' underlying information needs and preferences. In my first essay I specifically focus on big-ticket, high-involvement goods for which firms essentially have sparse information on their potential buyers - making information reflected in consumers' online search very valuable to online retailers. I use a new and rich source of clickstream data obtained from a leading clicks-and-mortar retailer to model consumers' purchase outcomes as a function of the product and price information provided by the retailer, and find interesting differences for sessions belonging to customers classified as browsers, directed shoppers and deliberating researchers. Since consumers typically straddle online as well as traditional channels, the second essay in my dissertation examines how online information acquired by consumers affects their choices in offline used-good markets. Secondary markets characterized by information asymmetries have typically resorted to quality-signaling mechanisms such as certification to help reduce the associated frictions. However, the value of traditional quality signals to consumers depends crucially on the extent of the asymmetries in these markets. The online information available to consumers today may help bridge such asymmetries. Drawing upon a unique and extensive dataset of over 12,000 consumers who purchased used vehicles, I examine the impact of their information acquisition from online intermediaries on their choice of (reliance on) one such quality signal - certification, as well as the price paid. These findings will help firms to better understand how the provision of different types of online information impacts consumers' choices and outcomes, and therefore help them in designing better and targeted strategies to interact with consumers
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