Related papers: Can Users Fix Algorithms? A Game-Theoretic Analysi…

We design mechanisms for maintaining public goods which require periodic in-kind contributions, motivated by incentives problems facing crowd-sourced recommender systems. Utilitarian welfare is maximized by redistributive policies which are…

In this paper, we introduce new formal methods and provide empirical evidence to highlight a unique safety concern prevalent in reinforcement learning (RL)-based recommendation algorithms -- 'user tampering.' User tampering is a situation…

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based…

Social media broadly refers to digital platforms and applications that simulate social interactions online. This study investigates the impact of social media platforms and their algorithms on political interest among users. As social media…

Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' competition impacts user welfare and how the relevance-driven…

Recommender system exists everywhere in the business world. From Goodreads to TikTok, customers of internet products become more addicted to the products thanks to the technology. Industrial practitioners focus on increasing the technical…

Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms…

Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to the predicted…

Many current applications use recommendations in order to modify the natural user behavior, such as to increase the number of sales or the time spent on a website. This results in a gap between the final recommendation objective and the…

AI-based social media platforms has already transformed the nature of economic and social interaction. AI enables the massive scale and highly personalized nature of online information sharing that we now take for granted. Extensive…

Social media platforms are ecosystems in which many decisions are constantly made for the benefit of the creators in order to maximize engagement, which leads to a maximization of income. The decisions, ranging from collaboration to public…

Online platforms such as YouTube, Instagram heavily rely on recommender systems to decide what content to present to users. Producers, in turn, often create content that is likely to be recommended to users and have users engage with it. To…

Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content…

Information is transmitted through websites, and immediate reactions to various kinds of information are required. Hence, efforts by users to select information themselves have increased, which is fueling further improvements in…

Recommender systems have become increasingly important with the rise of the web as a medium for electronic and business transactions. One of the key drivers of this technology is the ease with which users can provide feedback about their…

AI based social media recommendations have great potential to improve the user experience. However, often these recommendations do not match the user interest and create an unpleasant experience for the users. Moreover, the recommendation…

Many studies in recommender systems (RecSys) adopt a general problem definition, i.e., to recommend preferred items to users based on past interactions. Such abstraction often lacks the domain-specific nuances necessary for practical…

Recommender systems shape online interactions by matching users with creators content to maximize engagement. Creators, in turn, adapt their content to align with users preferences and enhance their popularity. At the same time, users…

Popularity systems, like Twitter retweets, Reddit upvotes, and Pinterest pins have the potential to guide people toward posts that others liked. That, however, creates a feedback loop that reduces their informativeness: items marked as more…

In many online platforms, customers' decisions are substantially influenced by product rankings as most customers only examine a few top-ranked products. Concurrently, such platforms also use the same data corresponding to customers'…