A Taxonomy of Web Personalization

Web personalization has become an important way to provide individualized user experiences. As a fragmented use of the term “Web personalization” and a lack of a common framework potentially hinder the establishment of a cumulative body of research, we develop a taxonomy of Web personalization. Bringing together research from information systems, computer science, and marketing, we develop a taxonomy focusing on the meta-characteristics user modeling (with the dimensions type of data, acquisition method, and life span of data) and system adaptation (with the dimensions object, volatility, scope, and control of adaptation). We demonstrate an application of our taxonomy by analyzing a sample of articles published in premier information systems journals and present some exemplary use cases to demonstrate how the taxonomy could be applied in practical contexts

Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

This paper presents an experiment on newsreaders’ behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user’s interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers’ perceived satisfaction.Paula Viana and Márcio Soares were partial supported by Project “TEC4Growth—Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020”, under Research Line FourEyes, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Paula Viana has also been supported by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. Rita Gaio was partially supported by CMUP, which is Financed by national funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., under the project with the reference UIDB/00144/2020. Amílcar Correia was partially supported by the Project Pglobal (Nr. 2014/38592-Programa Operacional Temático Factores de Competitividade/Programa Operacional do Norte, Funded by ERDF).info:eu-repo/semantics/publishedVersio

The Personalization Puzzle

Complex algorithms determine users’ search results and the content of their social media accounts. These algorithms often use machine learning and artificial intelligence, making it impossible to predict their output. Increasingly, these algorithms have been employed to personalize users’ online experiences. Google and Facebook use these algorithms to analyze users’ likes, clicks, search history, location, and other information to determine which articles, websites, and posts to include in search results and newsfeeds. Often users are completely unaware of the algorithms operating beneath the surface, controlling the information they receive. This lack of transparency makes it difficult for users to access the unbiased information necessary to make decisions, which is a key requirement for effective self-government. As web personalization becomes more prominent, it will challenge one of the fundamental basis of our democratic society; the access to unbiased information. By creating online echo chambers that present users with information that confirm their beliefs, theories, and biases, personalization stifles open discussion and debate. We need to balance Google’s and Facebook’s rights to free speech with access to diverse and contradictory information. In stark contrast to the dystopias imagined by Orwell and Huxley, it is not the government that threatens our individual rights via control, surveillance, and censorship, but private corporations, which are not bound by the First Amendment. Although the First Amendment does not prevent corporations from stifling speech, the rights and values promoted by the First Amendment should be the starting point of our analysis. Online personalization threatens our freedom of expression, which is critical to democratic debate and innovation

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table

Platform Advocacy and the Threat to Deliberative Democracy

Businesses have long tried to influence political outcomes, but today, there is a new and potent form of corporate political power—Platform Advocacy. Internet-based platforms, such as Facebook, Google, and Uber, mobilize their user bases through direct solicitation of support and the more troubling exploitation of irrational behavior. Platform Advocacy helps platforms push policy agendas that create favorable legal environments for themselves, thereby strengthening their own dominance in the marketplace. This new form of advocacy will have radical effects on deliberative democracy. In the age of constant digital noise and uncertainty, it is more important than ever to detect and analyze new forms of political power. This Article will contribute to our understanding of one such new form and provide a way forward to ensure the exceptional power of platforms do not improperly influence consumers and, by extension, lawmakers

Platforms, the First Amendment and Online Speech: Regulating the Filters

In recent years, online platforms have given rise to multiple discussions about what their role is, what their role should be, and whether they should be regulated. The complex nature of these private entities makes it very challenging to place them in a single descriptive category with existing rules. In today’s information environment, social media platforms have become a platform press by providing hosting as well as navigation and delivery of public expression, much of which is done through machine learning algorithms. This article argues that there is a subset of algorithms that social media platforms use to filter public expression, which can be regulated without constitutional objections. A distinction is drawn between algorithms that curate speech for hosting purposes and those that curate for navigation purposes, and it is argued that content navigation algorithms, because of their function, deserve separate constitutional treatment. By analyzing the platforms’ functions independently from one another, this paper constructs a doctrinal and normative framework that can be used to navigate some of the complexity. The First Amendment makes it problematic to interfere with how platforms decide what to host because algorithms that implement content moderation policies perform functions analogous to an editorial role when deciding whether content should be censored or allowed on the platform. Content navigation algorithms, on the other hand, do not face the same doctrinal challenges; they operate outside of the public discourse as mere information conduits and are thus not subject to core First Amendment doctrine. Their function is to facilitate the flow of information to an audience, which in turn participates in public discourse; if they have any constitutional status, it is derived from the value they provide to their audience as a delivery mechanism of information. This article asserts that we should regulate content navigation algorithms to an extent. They undermine the notion of autonomous choice in the selection and consumption of content, and their role in today’s information environment is not aligned with a functioning marketplace of ideas and the prerequisites for citizens in a democratic society to perform their civic duties. The paper concludes that any regulation directed to content navigation algorithms should be subject to a lower standard of scrutiny, similar to the standard for commercial speech

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System