A Survey on True-reputation Algorithm for Trustworthy Online Rating System

The average of customer ratings on a product, which we call a reputation, is one of the key factors in online shoping. The common way for customers to express their satisfaction level with their purchases is through online ratings. The overall buyer?s satisfaction is quantified as the aggregated score of all ratings and is available to all buyers. This average score and reputation of a product acts as a guide for online buyers and highly influences consumer?s final purchase decisions. The trustworthiness of a reputation can be achieved when a large number of buyers involved in ratings with honesty. If some users wantedly give unfair ratings to a item, especially when few users have participated, the reputation of the product could easily be modified. In order to improve the trustworthiness of the products in e-commerce sites a new model is proposed with a true - reputation algorithm that repeatedly adjusts the reputation based on the confidence of the user ratings

TO EXPLAIN OR NOT TO EXPLAIN: AN EMPIRICAL INVESTIGATION OF AI-BASED RECOMMENDATIONS ON SOCIAL MEDIA PLATFORMS

AI-based social media recommendations have a great potential to improve user experience. However, often these recommendations do not match the user interest and create an unpleasant experience for the users. Moreover, the recommendation system being blackbox creates comprehensibility and transparency issues. This paper investigates social media recommendations from an end-user perspective. For the investigation, we used the popular social media platform Facebook and recruited regular users to conduct a qualitative analysis. We asked participants about the social media content suggestions, their comprehensibility, and explainability. Our analysis shows users mostly require explanation whenever they encounter unfamiliar content and to ensure their online data security. Furthermore, the users require concise, non-technical explanations along with the facility of controlled information flow. In addition, we observed that explanations impact the user’s perception of transparency, trust, and understandability. Finally, we have outlined some design implications and presented a synthesized framework based on our data analysis

Recommendation Systems: A Systematic Review

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date:August 31st 202

On Measuring Bias in Online Information

Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.Comment: 6 pages, 1 figur