1,721,030 research outputs found
Privacy and Big Data: Scalable Approaches to Sanitize Large Transactional Databases for Sharing
No full textFull text access from Treasures at UT Dallas is restricted to current UTD affiliates.Scalability and privacy form two critical dimensions that will eventually determine the extent of the success of big data analytics. We present scalable approaches to address privacy concerns when sharing transactional databases. Although the benefits of sharing are well documented and the number of firms sharing transactional data has increased over the years, the rate at which this number has grown is not quite what it could have been. Concerns about revealing proprietary information have prevented some retailers from sharing, despite the obvious advantages in an increasingly networked economy. In the context of sharing transactional data, sensitive information is typically based on relationships derived from frequently occurring itemsets, result of surprisingly successful promotions by the retailer, or unexpected relationships identified by the retailer while mining the data. Prior work in this area includes optimal approaches based on integer programming to maximize the accuracy of shared databases, while hiding all sensitive itemsets. While these approaches were shown to solve problems involving up to 10 million transactions, many transactional databases in the big data context are considerably larger and the existing integer programming-based procedures do not scale well enough to solve these larger problems. Consequently, there is no effective solution procedure for such databases in extant literature.
In this paper, we first present an optimal procedure leveraging intuition from linear programming based column generation. Next, we identify a common structure that exists in these problems, and show how it can be taken advantage of through an approach based on sorting and column generation to make the process more efficient. We then illustrate how this structure can be incorporated into the column generation based procedure to develop an effective, scalable heuristic. Computational experiments are conducted on databases with 50 million and 100 million transactions, involving problems that could not be solved using existing optimal procedures. These experiments show that the optimal column generation based procedure can solve problem instances significantly larger than those tackled previously, and that the scalable heuristic identifies nearoptimal solutions quickly in all instances where the optimal solution is known. We investigate the impact of hiding sensitive itemsets on the quality of a rule-based recommender system derived from the shared data. As expected, recommendation quality decreases as the number of sensitive itemsets increases; however, recommendation accuracy stays above 80% of the original rate when using the unmodified data even when there are 1,000 sensitive itemsets to hide. The effect on recommendation accuracy from using the heuristic relative to the optimal approach was very small: the accuracies with the heuristic were over 97% of the corresponding accuracies with the optimal approach in every experiment, and over 99% in the vast majority.Naveen Jindal School of Managemen
Firm Competitive Structure and Consumer Reaction in Search Advertisings
Full text linkSponsored search advertising has become an important venue for firms competing for consumers. As a result, many keywords attract a large number of bidders, and the competing advertisers may be quite heterogeneous. We examine whether this heterogeneity impacts how consumers perceive and react to such competitions. To this end, we draw on the theory of strategic groups to prescribe the structure of the competitive environment and investigate how strategic groups impact consumers’ clicking and website-visit behavior when viewing sponsored search results. Our unique datasets that combine search results from Google and consumers’ clickstream data enable us to disentangle such an impact. We find strong positive externality for within-group competitors relative to across-group competitors: (1) consumers are more likely to co-visit two firms that belong to the same strategic group, as opposed to two firms from different groups when both firms appear in the search results; (2) the presence of a firm in the search results primes consumers to visit other firms from the same strategic group even when the other firms do not appear in the search results. Our findings contribute to the sponsored search and strategic group literature by theorizing and empirically verifying consumers’ website-visit behaviors from the strategic group perspective.This article is published as Nie, Cheng; Zheng, Zhiqiang (Eric); and Sarkar, Sumit (2024) "Firm Competitive Structure and Consumer Reaction in Search Advertisings," Journal of the Association for Information Systems, 25(2), 442-462.
DOI: 10.17705/1jais.00835. Available at: https://aisel.aisnet.org/jais/vol25/iss2/2. Posted with permission
A Strategic Group Analysis of Competitor Behavior in Search Advertising
Full text linkFirms compete intensely in sponsored search. Their bidding strategies hinge on understanding who competes with whom, how they compete, and how consumers react to competing advertisements. In this context, we investigate how firm competition impacts consumers’ click-through behaviors in search advertising from a strategic group perspective. Using search results from Google and consumers’ clickstream data, we found strong negative externality for competitors within the same strategic group relative to competitors across strategic groups: firms reap fewer click-throughs when an advertisement of another firm from the same strategic group is also displayed in search results, relative to when other displayed advertisers are not from the same group. This indicates that when competitors from the same strategic group are likely to appear in the results of a sponsored search auction, the focal firm would be better off avoiding head-to-head competition in the auction. However, we did not find empirical evidence of such firm behaviors, suggesting myopia or the inability of firms to avoid such competition. We also show that when multiple firms from the same strategic group appear in search results, the closer the focal firm is located to such competing firms, the more click-throughs the firm accrues. This suggests that firms should stay close to their within-group competitors when they compete in the same search auction. Further, our empirical results indicate that firms are indeed doing so. Using another set of data from Google AdWords reports, we show that our findings are also robust to multi-keyword bidding scenarios. These findings represent the first attempt to understand the impact of strategic groups in search advertising and provide interesting implications for advertisers and search engines.This article is published as Nie, Cheng; Zheng, Zhiqiang (Eric); and Sarkar, Sumit (2021) "A Strategic Group Analysis of Competitor Behavior in Search Advertising," Journal of the Association for Information Systems, 22(6), 1659-1685. DOI: 10.17705/1jais.00710
Available at: https://aisel.aisnet.org/jais/vol22/iss6/5. Posted with permission
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Rebellious wives and dysfunctional marriages: Indian women's discourses and participation in the debates over restitution of conjugal rights and the child marriage controversy in the 1880s and 1890s
No full textEssays on Online Review Manipulation and Sponsored Search Advertising
No full textOnline reviews have been found to be important drivers for consumer’s purchase decisions. Many firms resort to online review manipulations in the hope to improve their revenues. Manipulated online reviews have been acknowledged as a critical challenge in the e-commerce industry. Two essays in my dissertation are in the domain of online review manipulation in the hotel industry. In the first essay, I study how a new type of competition from the sharing economy (specifically Airbnb.com), impacts how hotels manipulate reviews. Surprisingly, I find that hotels demote their competitors less in the presence of higher levels of Airbnb competition. For self-promotion, Airbnb’s impact varies across hotel types – high-end hotels intensify self-promotion activities while low-end hotels make no change. In the second essay, I study the economic effectiveness of different review manipulation strategies. I find that high-end hotels indeed benefit from self-promotion and are hurt from getting demoted by other hotels. Moreover, the negative impact of getting demoted is stronger for low-end hotels than for high-end hotels.
My third essay is in the sponsored search advertising domain. I draw on the theory of strategic groups to investigate how advertisements from firms within such groups impact consumers’ clickstream behavior when viewing sponsored search results. I find a strong positive externality for within-group competitors relative to across-group competitors. My findings contribute to the sponsored search literature by theorizing and empirically verifying consumers’ search behaviors from the strategic group perspective
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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