Challenges and research opportunities in ecommerce search and recommendations
With the rapid adoption of online shopping, academic research in the eCommerce domain
has gained traction. However, significant research challenges remain, spanning from classic …
has gained traction. However, significant research challenges remain, spanning from classic …
Causerec: Counterfactual user sequence synthesis for sequential recommendation
Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …
recommender systems. Recent advances in sequential recommenders have convincingly …
Denoising implicit feedback for recommendation
The ubiquity of implicit feedback makes them the default choice to build online
recommender systems. While the large volume of implicit feedback alleviates the data …
recommender systems. While the large volume of implicit feedback alleviates the data …
Entity summarization: State of the art and future challenges
The increasing availability of semantic data has substantially enhanced Web applications.
Semantic data such as RDF data is commonly represented as entity-property-value triples …
Semantic data such as RDF data is commonly represented as entity-property-value triples …
Self-guided learning to denoise for robust recommendation
The ubiquity of implicit feedback makes them the default choice to build modern
recommender systems. Generally speaking, observed interactions are considered as …
recommender systems. Generally speaking, observed interactions are considered as …
Towards robust neural graph collaborative filtering via structure denoising and embedding perturbation
Neural graph collaborative filtering has received great recent attention due to its power of
encoding the high-order neighborhood via the backbone graph neural networks. However …
encoding the high-order neighborhood via the backbone graph neural networks. However …
When inverse propensity scoring does not work: Affine corrections for unbiased learning to rank
Besides position bias, which has been well-studied, trust bias is another type of bias
prevalent in user interactions with rankings: users are more likely to click incorrectly wrt their …
prevalent in user interactions with rankings: users are more likely to click incorrectly wrt their …
Unifying online and counterfactual learning to rank: A novel counterfactual estimator that effectively utilizes online interventions
H Oosterhuis, M de Rijke - Proceedings of the 14th ACM international …, 2021 - dl.acm.org
Optimizing ranking systems based on user interactions is a well-studied problem. State-of-
the-art methods for optimizing ranking systems based on user interactions are divided into …
the-art methods for optimizing ranking systems based on user interactions are divided into …
Unbiased learning to rank: online or offline?
How to obtain an unbiased ranking model by learning to rank with biased user feedback is
an important research question for IR. Existing work on unbiased learning to rank (ULTR) …
an important research question for IR. Existing work on unbiased learning to rank (ULTR) …
Policy-aware unbiased learning to rank for top-k rankings
H Oosterhuis, M de Rijke - Proceedings of the 43rd International ACM …, 2020 - dl.acm.org
Counterfactual Learning to Rank (LTR) methods optimize ranking systems using logged
user interactions that contain interaction biases. Existing methods are only unbiased if users …
user interactions that contain interaction biases. Existing methods are only unbiased if users …