Related papers: Towards Content Provider Aware Recommender Systems…
Studying how recommendation systems reshape social networks is difficult on live platforms: confounds abound, and controlled experiments risk user harm. We present an agent-based simulator where content production, tie formation, and a…
Traditional recommender systems present a relatively static list of recommendations to a user where the feedback is typically limited to an accept/reject or a rating model. However, these simple modes of feedback may only provide limited…
Recommender systems have been successfully applied to assist decision making by producing a list of item recommendations tailored to user preferences. Traditional recommender systems only focus on optimizing the utility of the end users who…
Recommender systems apply data mining techniques and prediction algorithms to predict users' interest on information, products and services among the tremendous amount of available items. The vast growth of information on the Internet as…
Conversational information access is an emerging research area. Currently, human evaluation is used for end-to-end system evaluation, which is both very time and resource intensive at scale, and thus becomes a bottleneck of progress. As an…
Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production.Traditional recommender models mostly collect as comprehensive as possible user behaviors for…
Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users. In reality, however, various…
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, the defect of recommendation algorithms may put users in very…
Smart recommendation algorithms have revolutionized content delivery and improved efficiency across various domains. However, concerns about user agency arise from the algorithms' inherent opacity (information asymmetry) and one-way output…
Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize…
In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem…
Current personalized recommender systems predominantly rely on static offline data for algorithm design and evaluation, significantly limiting their ability to capture long-term user preference evolution and social influence dynamics in…
The number of Internet users had grown rapidly enticing companies and cooperations to make full use of recommendation infrastructures. Consequently, online advertisement companies emerged to aid us in the presence of numerous items and…
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Specifically, we present our system architecture that utilizes…
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however, in terms of cycle…
Recommender ecosystems are an emerging subject of research. Such research examines how the characteristics of algorithms, recommendation consumers, and item providers influence system dynamics and long-term outcomes. One architectural…
Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs. However, evaluating the effectiveness of these…
A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households' energy efficiency. To nudge changes in energy consumption behavior, simple but…
Social media platforms provide valuable opportunities for users to gather information, interact with friends, and enjoy entertainment. However, their addictive potential poses significant challenges, including overuse and negative…
Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as "connecting relevant content to interested users". Personalized recommendation algorithms achieve this goal by…