Latest trends of security and privacy in recommender systems: a comprehensive review and future perspectives
With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
The unfairness of popularity bias in recommendation
Recommender systems are known to suffer from the popularity bias problem: popular (ie
frequently rated) items get a lot of exposure while less popular ones are under-represented …
frequently rated) items get a lot of exposure while less popular ones are under-represented …
Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation
Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …
problem and provide accurate and tailored recommendations. However, the impressive …
Modeling recommender ecosystems: Research challenges at the intersection of mechanism design, reinforcement learning and generative models
Modern recommender systems lie at the heart of complex ecosystems that couple the
behavior of users, content providers, advertisers, and other actors. Despite this, the focus of …
behavior of users, content providers, advertisers, and other actors. Despite this, the focus of …
Measuring fairness in ranked results: an analytical and empirical comparison
Information access systems, such as search and recommender systems, often use ranked
lists to present results believed to be relevant to the user's information need. Evaluating …
lists to present results believed to be relevant to the user's information need. Evaluating …
[HTML][HTML] Jointly modeling intra-and inter-session dependencies with graph neural networks for session-based recommendations
Recently, graph neural networks (GNNs) have achieved promising results in session-based
recommendation. Existing methods typically construct a local session graph and a global …
recommendation. Existing methods typically construct a local session graph and a global …
Quantifying and mitigating popularity bias in conversational recommender systems
Conversational recommender systems (CRS) have shown great success in accurately
capturing a user's current and detailed preference through the multi-round interaction cycle …
capturing a user's current and detailed preference through the multi-round interaction cycle …
Fairsr: Fairness-aware sequential recommendation through multi-task learning with preference graph embeddings
Sequential recommendation (SR) learns from the temporal dynamics of user-item
interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of …
interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of …
Going beyond popularity and positivity bias: Correcting for multifactorial bias in recommender systems
Two typical forms of bias in user interaction data with recommender systems (RSs) are
popularity bias and positivity bias, which manifest themselves as the over-representation of …
popularity bias and positivity bias, which manifest themselves as the over-representation of …