Analysis of Probabilistic News Recommender Systems

The focus of this research is the N “most popular” (Top-N) news recommender systems (NRS), widely used by media sites (e.g. New York Times, BBC, Wall Street Journal all prominently use this). This common recommendation process is known to have major limitations in terms of creating artificial amplification in the counts of recommended articles and that it is easily susceptible to manipulation. To address these issues, probabilistic NRS has been introduced. One drawback of the probabilistic recommendations is that it potentially chooses articles to recommend that might not be in the current “best” list. However, the probabilistic selection of news articles is highly robust towards common manipulation strategies. This paper compares the two variants of NRS (Top-N and probabilistic) based on (1) accuracy loss (2) distortion in counts of articles due to NRS and (3) comparison of probabilistic NRS with an adapted influence limiter heuristic

Customer empowerment in tourism through Consumer Centric Marketing (CCM)

We explain Consumer Centric Marketing (CCM) and adopt this new technique to travel context. Benefits and disadvantages of the CCM are outlined together with warnings of typical caveats Value: CCM will be expected as the norm in the travel industry by customers of the future, yet it is only the innovators who gain real tangible benefits from this development. We outline current and future opportunities to truly place your customer at the centre and provide the organisation with some real savings/gains through the use of ICT Practical Implications: We offer tangible examples for travel industry on how to utilise this new technology. The technology is already available and the ICT companies are keen to establish ways how consumers can utilise it, i.e. by providing ‘content’ for these ICT products the travel industry can fully gain from these developments and also enhance consumers’ gains from it. This can result in more satisfied customers for the travel (as well as ICT) companies thus truly adopting the basic philosophy of marketin

Incentive-Centered Design for User-Contributed Content

We review incentive-centered design for user-contributed content (UCC) on the Internet. UCC systems, produced (in part) through voluntary contributions made by non-employees, face fundamental incentives problems. In particular, to succeed, users need to be motivated to contribute in the first place ("getting stuff in"). Further, given heterogeneity in content quality and variety, the degree of success will depend on incentives to contribute a desirable mix of quality and variety ("getting \emph{good} stuff in"). Third, because UCC systems generally function as open-access publishing platforms, there is a need to prevent or reduce the amount of negative value (polluting or manipulating) content. The work to date on incentives problems facing UCC is limited and uneven in coverage. Much of the empirical research concerns specific settings and does not provide readily generalizable results. And, although there are well-developed theoretical literatures on, for example, the private provision of public goods (the "getting stuff in" problem), this literature is only applicable to UCC in a limited way because it focuses on contributions of (homogeneous) money, and thus does not address the many problems associated with heterogeneous information content contributions (the "getting \emph{good} stuff in" problem). We believe that our review of the literature has identified more open questions for research than it has pointed to known results.http://deepblue.lib.umich.edu/bitstream/2027.42/100229/1/icd4ucc.pdf7

The audience response to different referral reward programs’ designs in social networking sites

The growing connectivity of customers through Social Networking Sites (SNSs), the increasing acknowledgment of the power of online reviews, and the enrichment of brand-consumer relations online have led to a rise in interest around electronic word of mouth (eWOM). These realizations led marketers to embrace strategies to stimulate and amplify eWOM, and one common technique is the delivery incentives (e.g., rewards). Expanding research show that the design of incentivized eWOM programs, namely Referral Reward Programs (RRPs), is expected to determine the overall effectiveness of those programs. To be successful, RRPs need a high likelihood of referral from the referral provider and a high receptivity from the referral receiver. Thus, this thesis further examines the recipient's perspective and role in RRPs in Social Networking Sites. The main goal of this dissertation is to analyze the impact of different reward allocations and tie strength, i.e., the relationship between the recommender and the receiver, on eWOM receivers' responses to RRPs. To do so, this thesis drew upon the Persuasion Knowledge Model to analyze these relations, mainly focusing on three RRPs outcomes: review credibility, brand attitude, and purchase intentions. To extract relevant conclusions, a research model and hypothesis were developed, based on a previously elaborated literature review, containing the main concepts, theories, and models that hold the present research. An experimental design was conducted employing an online questionnaire to test the research model, which gathered 526 responses. Finally, the results were discussed, and both theoretical and practical implications were deduced.A crescente conectividade entre consumidores, a gradual descoberta do poder das recomendações, e o enriquecimento das relações marca-consumidor por meio de Sites de Redes Sociais, levaram a um crescente interesse em torno do passa-a-palavra eletrónico. Consequentemente, os profissionais de marketing começaram a adotar estratégias para estimular e ampliar essa poderosa ferramenta. Uma técnica comum é a oferta de incentivos (por exemplo, recompensas). A literatura mostra que a estrutura de um programa de passa-a-palavra eletrónico incentivado, nomeadamente, de Programas de Recompensa por Referência, é fundamental para a eficácia dos mesmos. Reconhecendo que, para serem eficazes, os Programas de Referência por Recompensa precisam, tanto da iniciativa do transmissor, como da adesão do recetor, esta dissertação explora a perspetiva e o papel do recetor nestes programas, em Sites de Redes Sociais. Deste modo, o seu principal objetivo é analisar o impacto de diferentes alocações de recompensas e forças das ligações (i.e., relação entre o transmissor e o recetor) nas respostas dos recetores a Programas de Referência por Recompensa. Para tal, o Modelo de Conhecimento de Persuasão foi utilizado a fim de analisar três indicadores: credibilidade da recomendação, atitude perante a marca e intenção de compra. Para extrair conclusões relevantes, foram desenvolvidos um modelo conceptual e um conjunto de hipóteses, com base numa revisão da literatura que aborda os principais conceitos, teorias e modelos que sustentam a presente pesquisa. A posteriori, foi realizado um questionário online, que reuniu 526 respostas. Por último, os resultados foram discutidos e as implicações teóricas e práticas foram apresentadas

Bots influence opinion dynamics without direct human-bot interaction: The mediating role of recommender systems

Bots' ability to influence public discourse is difficult to estimate. Recent studies found that hyperpartisan bots are unlikely to influence public opinion because bots often interact with already highly polarized users. However, previous studies focused on direct human-bot interactions (e.g., retweets, at-mentions, and likes). The present study suggests that political bots, zealots, and trolls may indirectly affect people's views via a platform's content recommendation system's mediating role, thus influencing opinions without direct human-bot interaction. Using an agent-based opinion dynamics simulation, we isolated the effect of a single bot-representing 1% of nodes in a network-on the opinion of rational Bayesian agents when a simple recommendation system mediates the agents' content consumption. We compare this experimental condition with an identical baseline condition where such a bot is absent. Across conditions, we use the same random seed and a psychologically realistic Bayesian opinion update rule so that conditions remain identical except for the bot presence. Results show that, even with limited direct interactions, the mere presence of the bot is sufficient to shift the average population's opinion. Virtually all nodes -not only nodes directly interacting with the bot- shifted towards more extreme opinions. Furthermore, the mere bot's presence significantly affected the internal representation of the recommender system. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence population opinions not only by directly interacting with humans but also by secondary effects, such as shifting platforms recommendation engines internal representations. The mediating role of recommender systems creates indirect causal pathways of algorithmic opinion manipulation.The study was funded by the Max Planck Institute for Human Development. D.B. was partly funded by a research grant from the Institute of Psychology at the Chinese Academy of Sciences

Marketing Intelligence: Boom or Bust of Service Marketing?

Marketing intelligence fosters two major developments within digital service marketing. On the one hand, a boom of services seems to have evolved, accelerated by the opportunities of marketing intelligence. It has contributed to the optimization of customer experiences, e.g., supported by mobile, personalized, and customized marketing services. On the other hand, (digital) self-services are likely to pervert the term “service”. Lifecycle marketing, including annoying marketing communication in real-time, automated price adjustment and programmatic advertising based on artificial intelligence, affects the vision of fully standardized marketing automation. Additionally, there are incentives to pollute the digital information in order to manufacture opinions. Fake news is one popular example. This leads to the (open) question if marketing intelligence means service boom or bust of marketing. This contribution aims to elaborate the boom-and-bust aspects of marketing intelligence and suggests a trade-off. The method applied in this paper will be a descriptive and conceptual literature review, through which the paradigmatic thoughts will be juxtaposed from the perspective of service

The Ethics of Algorithmic Outsourcing in Everyday Life

We live in a world in which ‘smart’ algorithmic tools are regularly used to structure and control our choice environments. They do so by affecting the options with which we are presented and the choices that we are encouraged or able to make. Many of us make use of these tools in our daily lives, using them to solve personal problems and fulfill goals and ambitions. What consequences does this have for individual autonomy and how should our legal and regulatory systems respond? This chapter defends three claims by way of response. First, it argues that autonomy is indeed under threat in some new and interesting ways. Second, it evaluates and disputes the claim that we shouldn’t overestimate these new threats because the technology is just an old wolf in a new sheep’s clothing. Third, and finally, it looks at responses to these threats at both the individual and societal level and argues that although we shouldn’t encourage an attitude of ‘helplessness’ among the users of algorithmic tools there is an important role for legal and regulatory responses to these threats that go beyond what are currently on offer

Don’t Take It Personally: Resistance to Individually Targeted Recommendations from Conversational Recommender Agents

Conversational recommender agents are artificially intelligent recommender systems that provide users with individually-tailored recommendations by targeting individual needs and communicating in a flowing dialogue. These are widely available online, communicating with users while demonstrating human-like (anthropomorphic) social cues. Nevertheless, little is known about the effect of their anthropomorphic cues on users’ resistance to the system and recommendations. Accordingly, this study examined the extent to which conversational recommender agents’ anthropomorphic cues and the type of recommendations provided (user-initiated and system-initiated) influenced users’ perceptions of control, trustworthiness, and the risk of using the platform. The study assessed how these perceptions, in turn, influence users’ adherence to the recommendations. An online experiment was conducted among users with conversational recommender agents and web recommender platforms that provided user-initiated or system-initiated restaurant recommendations. The results entail that user-initiated recommendations, compared to system-initiated, are less likely to affect users’ resistance to the system and are more likely to affect their adherence to the recommendations provided. Furthermore, the study’s findings suggest that these effects are amplified for conversational recommender agents, demonstrating anthropomorphic cues, in contrast to traditional systems as web recommender platforms

Computer-Supported Collaborative Production

This paper proposes the concept of collaborative production as a focus of concern within the general area of collaborative work. We position the concept with respect to McGrath's framework for small group dynamics and the more familiar collaboration processes of awareness, coordination, and communication (McGrath 1991). After reviewing research issues and computer-based support for these interacting aspects of collaboration, we turn to a discussion of implications for how to design improved support for collaborative production. We illustrate both the challenges of collaborative production and our design implications with a collaborative map-updating scenario drawn from the work domain of geographical information systems