ACM Transactions on Information Systems

Modern transformer-based information retrieval models achieve state-of-the-art performance across various benchmarks. The self-attention of the transformer models is a powerful mechanism to contextualize terms over the whole input but quickly becomes ...

Retrieval with extremely long queries and documents is a well-known and challenging task in information retrieval and is commonly known as Query-by-Document (QBD) retrieval. Specifically designed Transformer models that can handle long input sequences ...

Sparse representation learning based on Pre-trained Language Models has seen a growing interest in Information Retrieval. Such approaches can take advantage of the proven efficiency of inverted indexes and inherit desirable IR priors such as explicit ...

Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resources and latency.

We propose Fast-Forward indexes—vector forward indexes ...

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval ...

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. ...

Casting sequential recommendation (SR) as a reinforcement learning (RL) problem is promising and some RL-based methods have been proposed for SR. However, these models are sub-optimal due to the following limitations: (a) they fail to leverage the ...

Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can ...

We present two empirical studies to investigate users’ expectations and behaviours when using digital assistants, such as Alexa and Google Home, in a kitchen context: First, a survey (N = 200) queries participants on their expectations for the kinds of ...

Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. While this generally improves performance on average, the extra neurons do not ...

Target-oriented proactive dialogue systems aim at leading conversations from a dialogue context toward a pre-determined target, such as making recommendations on designated items or introducing new specific topics. To this end, it is critical for such ...

Recently, federated recommendation has become a research hotspot mainly because of users’ awareness of privacy in data. As a recent and important recommendation problem, in heterogeneous one-class collaborative filtering (HOCCF), each user may involve of ...

Search result diversification plays a crucial role in improving users’ search experience by providing users with documents covering more subtopics. Previous studies have made great progress in leveraging inter-document interactions to measure the ...

Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous ...

Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is ...

Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which have played crucial roles in various applications. Recently, many graph neural networks based approaches are proposed to model the graph structure information in ...

Meta-learning has recently promoted few-shot text classification, which identifies target classes based on information transferred from source classes through a series of small tasks or episodes. Existing works constructing their meta-learner on ...

Fairness has been gradually recognized as a significant problem in the recommendation domain. Previous models usually achieve fairness by reducing the average performance gap between different user groups. However, the average performance may not ...

Previous studies on sequential recommendation (SR) have predominantly concentrated on optimizing recommendation accuracy. However, there remains a significant gap in enhancing recommendation diversity, particularly for short interaction sequences. The ...

Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing GR models commonly employ maximum ...

Generating appropriate emotions for responses is essential for dialogue systems to provide human-like interaction in various application scenarios. Most previous dialogue systems tried to achieve this goal by learning empathetic manners from anonymous ...

As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models, since ...

Research on search result diversification strives to enhance the variety of subtopics within the list of search results. Existing studies usually treat a document as a whole and represent it with one fixed-length vector. However, considering that a long ...

Cross-domain Named Entity Recognition (NER) transfers knowledge learned from a rich-resource source domain to improve the learning in a low-resource target domain. Most existing works are designed based on the sequence labeling framework, defining entity ...

Information Extraction (IE) focuses on transforming unstructured data into structured knowledge, of which Named Entity Recognition (NER) is a fundamental component. In the realm of Information Retrieval (IR), effectively recognizing entities can ...

In educational data mining, knowledge tracing (KT) aims to model learning performance based on student knowledge mastery. Deep-learning-based KT models perform remarkably better than traditional KT and have attracted considerable attention. However, most ...

Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained considerable attention, arguing that results should not discriminate an individual or a sub-user group based on users’ sensitive attributes (e.g., gender). ...