Related papers: Information Cascades and Online Rating Games
Negative reviews on e-commerce platforms, mainly in the form of texts, are posted by online consumers to express complaints about unsatisfactory experiences, providing a proxy of big data for sellers to consider improvements. However, the…
Recommender systems have become integral to digital experiences, shaping user interactions and preferences across various platforms. Despite their widespread use, these systems often suffer from algorithmic biases that can lead to unfair…
The state of economic theory and accumulated facts from the different branches of the economic science require to analyze the concept of the description of economy systems. The economic reality generates the problems the solution of that is…
This paper presents two cases of random banking data generators based on migration matrices and scoring rules. The banking data generator is a new hope in researches of finding the proving method of comparisons of various credit scoring…
We present reputation-based mechanisms for building reliable task computing systems over the Internet. The most characteristic examples of such systems are the volunteer computing and the crowdsourcing platforms. In both examples end users…
Ranking problem has attracted much attention in real systems. How to design a robust ranking method is especially significant for online rating systems under the threat of spamming attacks. By building reputation systems for users, many…
Automatic sentiment analysis play vital role in decision making. Many organizations spend a lot of budget to understand their customer satisfaction by manually going over their feedback/comments or tweets. Automatic sentiment analysis can…
People often rely on online reviews to make purchase decisions. The present work aimed to gain an understanding of a machine learning model's prediction mechanism by visualizing the effect of sentiments extracted from online hotel reviews…
Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represent a valuable source…
Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are…
The evaluation of recommender system fairness has become increasingly important, especially with recent legislation that emphasises the development of fair and responsible artificial intelligence. This has led to the emergence of various…
Consumers often resort to third-party information such as word of mouth, testimonials and reviews to learn more about the quality of a new product. However, it may be difficult for consumers to assess the precision of such information. We…
The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of the people express their…
The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available in digital form. One…
Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective,…
Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an…
The effect of user bias in fact-checking has not been explored extensively from a user-experience perspective. We estimate the user bias as a function of the user's perceived reputation of the news sources (e.g., a user with liberal beliefs…
In Recommender System (RS), explanations help users understand why items are recommended and can enhance a system's transparency, persuasiveness, engagement, and trust, which are known as explanation goals. However, evaluating the…
We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of…
We analyze some voting models mimicking online evaluation systems intended to reduce the information overload. The minimum number of operations needed for a system to be effective is analytically estimated. When herding effects are present,…