Societal biases that are contained in retrieved documents have received increased interest.
Such biases, which are often prevalent in the training data and learned by the model, can …

This paper focuses on open-ended video question answering which aims to find the correct
answers from a large answer set in response to a video-related question. This is essentially …

The logs of the use of a search engine provide sufficient data to train a better ranker.
However, it is well known that such implicit feedback reflects biases, and in particular a …

Implicit recommendation refers to the users' feedback on items derived from their
interactions with items, ie, clicks, and purchases. The methods in the implicit …

Ranking is at the core of many artificial intelligence (AI) applications, including search
engines, recommender systems, etc. Modern ranking systems are often constructed with …

Ranking systems are ubiquitous in modern Internet services, including online marketplaces,
social media, and search engines. Traditionally, ranking systems only focus on how to get …

Extracting query-document relevance from the sparse, biased clickthrough log is among the
most fundamental tasks in the web search system. Prior art mainly learns a relevance …

Rankings are ubiquitous across many applications, from search engines to hiring
committees. In practice, many rankings are derived from the output of predictors. However …

Ranking systems are the key components of modern Information Retrieval (IR) applications,
such as search engines and recommender systems. Besides the ranking relevance to users …

Audio sentiment analysis is a growing area of research, however little attention has been
paid to the fairness of machine learning models in this field. Whilst the current literature …