Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of ...

In recent years, pairwise methods, such as Bayesian Personalized Ranking (BPR), have gained significant attention in the field of collaborative filtering for recommendation systems. Group BPR is an extension of BPR that incorporates user groups to relax ...

Pretrained language models such as BERT have been shown to be exceptionally effective for text ranking. However, there are limited studies on how to leverage more powerful sequence-to-sequence models such as T5. Existing attempts usually formulate text ...

Next item recommendation is a crucial task of session-based recommendation. However, the gap between the optimization objective (Binary Cross Entropy) and the ranking metric (Mean Reciprocal Rank) has not been well-explored, resulting in sub-optimal ...

User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by ...

Recent work has identified that distillation can be used to create vector quantization based ANN indexes by learning the inverted file index and product quantization. The argued advantage of using a fixed teacher encoder for queries and documents is that ...

Bandit algorithms for online learning to rank (OLTR) problems often aim to maximize long-term revenue by utilizing user feedback. From a practical point of view, however, such algorithms have a high risk of hurting user experience due to their aggressive ...

Ranking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of ranking would be achieved if we rank items according to their individual ...

Facebook content search is a critical channel that enables people to discover the best content to deepen their engagement with friends and family, creators, and communities. Building a highly personalized search engine to serve billions of daily active ...

Counterfactual learning to rank (CLTR) relies on exposure-based inverse propensity scoring (IPS), a LTR-specific adaptation of IPS to correct for position bias. While IPS can provide unbiased and consistent estimates, it often suffers from high variance. ...

User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to rank (ULTR) focuses on position bias, i.e., items at higher ranks are more ...

Many re-ranking strategies in search systems rely on stochastic ranking policies, encoded as Doubly-Stochastic (DS) matrices, that satisfy desired ranking constraints in expectation, e.g., Fairness of Exposure (FOE). These strategies are generally two-...

Developing a universal model that can efficiently and effectively respond to a wide range of information access requests-from retrieval to recommendation to question answering---has been a long-lasting goal in the information retrieval community. This ...