Finding the right product on e-commerce websites with millions of products is a daunting task for a large set of customers. On the search page, product attribute filters a.k.a. "refinements" emerge as a convenient navigational option for customers to ...

ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing ...

This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the ...

In this tutorial, we aim to shed light on the task of recommending a set of multiple items at once. In this scenario, historical interaction data between users and items could also be in the form of a sequence of interactions with sets of items. Complex ...

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 ...

This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the ...

Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (ie correlation) from users' behaviors. However, they still suffer from ...

This full-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and ...

Tasks are central to information retrieval (IR) and drive interactions with search systems [2, 4, 10]. Understanding and modeling tasks helps these systems better support user needs [8, 9, 11]. This keynote focuses on search tasks, the emergence of ...

Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations, these methods however implicitly deprioritize the modeling of user-wise ...

In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel Multi-...

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 ...

In recommender systems (RSs), inverse propensity score (IPS) has been a key technique to mitigate popularity bias by decreasing the contribution of popular items in modeling user-item interactions. However, conventional IPS treats all users equally, ...

A query performance prediction (QPP) method predicts the effectiveness of an IR system for a given query. While unsupervised approaches have been shown to work well for statistical IR models, it is likely that these approaches would yield limited ...

In this work, we present an unsupervised retrieval method with contrastive learning on web anchors. The anchor text describes the content that is referenced from the linked page. This shows similarities to search queries that aim to retrieve pertinent ...

In industrial recommendation systems, both data sizes and computational resources vary across different scenarios. For scenarios with limited data, data sparsity can lead to a decrease in model performance. Heterogeneous knowledge distillation-based ...

Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based ...

Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback. Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click ...

Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propensity weighting (IPW) or causal ...

The problem of signed network embedding (SNE) aims to represent nodes in a given signed network as low-dimensional vectors. While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly ...