Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Social Network Analysis

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

/pdf/a-survey-of-collaborative-filtering-techniques-19zxbumsgq.pdf

A survey of collaborative filtering techniques

Are trust and risk important in consumers' electronic commerce purchasing decisions? What are the antecedents of trust and risk in this context? How do trust and risk affect an Internet consumer's purchasing decision? To answer these questions, we i) develop a theoretical framework describing the trust-based decision-making process a consumer uses when making a purchase from a given site, ii) test the proposed model using a Structural Equation Modeling technique on Internet consumer purchasing behavior data collected via a Web survey, and iii) consider the implications of the model. The results of the study show that Internet consumers' trust and perceived risk have strong impacts on their purchasing decisions. Consumer disposition to trust, reputation, privacy concerns, security concerns, the information quality of the Website, and the company's reputation, have strong effects on Internet consumers' trust in the Website. Interestingly, the presence of a third-party seal did not strongly influence consumers' trust.

/pdf/a-trust-based-consumer-decision-making-model-in-electronic-2tir3ct0zz.pdf

A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.

/pdf/introduction-to-recommender-systems-handbook-4qtj503mvw.pdf

Introduction to Recommender Systems Handbook

Personalization refers to the tailoring of products and purchase experience to the tastes of individual consumers based upon their personal and preference information. Recent advances in information acquisition and processing technologies have allowed online vendors to offer varieties of web-based personalization that not only increases switching costs, but also serves as important means of acquiring valuable customer information. However, investments in online personalization may be severely undermined if consumers do not use these services due to privacy concerns. In the absence of any empirical evidence that seeks to understand this consumer dilemma, our research develops a parsimonious model to predict consumers' usage of online personalization as a result of the tradeoff between their value for personalization and concern for privacy. In addition to this tradeoff, we find that a consumer's intent to use personalization services is positively influenced by her trust in the vendor. Our findings suggest that: 1. online vendors can improve their abilities to acquire and use customer information through trust building activities; 2. it is of critical importance that vendors understand and evaluate the different values consumers may place in enjoying various types of personalization.

Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma

Electronic commerce (EC) transactions are subject to multiple information security threats. Proposes that consumer trust in EC transactions is influenced by perceived information security and distinguishes it from the objective assessment of security threats. Proposes mechanisms of encryption, protection, authentication, and verification as antecedents of perceived information security. These mechanisms are derived from technological solutions to security threats that are visible to consumers and hence contribute to actual consumer perceptions. Tests propositions in a study of 179 consumers and shows a significant relationship between consumers’ perceived information security and trust in EC transactions. Explores the role of limited financial liability as a surrogate for perceived security. However, the findings show that there is a minimal effect of financial liability on consumers’ trust in EC. Engenders several new insights regarding the role of perceived security in EC transactions.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2380722_code1325306.pdf?abstractid=2380722&mirid=1&type=2

Perceived Information Security, Financial Liability and Consumer Trust in Electronic Commerce Transactions

Perceived information security, financial liability and consumer trust in electronic commerce transactions

Digital goods lend themselves to versioning but also suffer from piracy losses. This paper develops a pricing model for digital experience goods in a segmented market and explores the optimality of sampling as a piracy-mitigating strategy. Consumers are aware of the true fit of an experience good to their tastes only after consumption, and as piracy offers an additional (albeit illegal) consumption opportunity, traditional segmentation findings from economics and sampling recommendations from marketing, need to be revisited. We develop a two-stage model of piracy for a market where consumers are heterogeneous in their marginal valuation for quality and their moral costs. In our model, some consumers pirate the product in the first stage allowing them to update their fit-perception that may result in re-evaluation of their buying/pirating decision in the second stage. We recommend distinct pricing and sampling strategies for underestimated and overestimated products and suggest that any potential benefits of piracy can be internalized through product sampling. Two counterintuitive results stand out. First, piracy losses are more severe for products that do not live up to their hype rather than for those that have been undervalued in the market, thus requiring a greater deterrence investment for the former, and second, unlike physical goods where sampling is always beneficial for underestimated products, sampling for digital goods is optimal only under narrowly defined circumstances due to the price boundaries created by both piracy and segmentation.

Managing Piracy: Pricing and Sampling Strategies for Digital Experience Goods in Vertically Segmented Markets

Consumer surveys show that online users value personalized content [5]. At the same time, providing personalization on websites seems quite profitable for web vendors [2, 6-8]. This win-win situation is however marred by privacy concerns since personalizing people's interaction entails gathering considerable amounts of data about them. As numerous recent surveys have consistently demonstrated, computer users are very concerned about their privacy on the Internet. Moreover, the collection of personal data is also subject to legal regulations in many countries and states. Both user concerns and privacy regulations impact frequently-used personalization methods. This workshop will explore the potential of research on "privacy-enhanced personalization," which aims at reconciling the goals and methods of user modeling and personalization with privacy constraints imposed by individual preferences, conventions and laws.

/pdf/privacy-enhanced-personalization-3rtapsjrh5.pdf

Ramnath K. Chellappa

Papers

Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma

Perceived Information Security, Financial Liability and Consumer Trust in Electronic Commerce Transactions

Perceived information security, financial liability and consumer trust in electronic commerce transactions

Managing Piracy: Pricing and Sampling Strategies for Digital Experience Goods in Vertically Segmented Markets

Privacy-enhanced personalization