ACM Transactions on Information Systems

Sequential recommendation systems aim to exploit users’ sequential behavior patterns to capture their interaction intentions and improve recommendation accuracy. Existing sequential recommendation methods mainly focus on modeling the items’ chronological ...

User behavior data, such as ratings and clicks, has been widely used to build personalizing models for recommender systems. However, many unflattering factors (e.g., popularity, ranking position, users’ selection) significantly affect the performance of ...

Document-level relation extraction (RE) aims to simultaneously predict relations (including no-relation cases denoted as NA) between all entity pairs in a document. It is typically formulated as a relation classification task with entities pre-detected in ...

A Session-Based Recommendation (SBR) seeks to predict users’ future item preferences by analyzing their interactions with previously clicked items. In recent approaches, Graph Neural Networks (GNNs) have been commonly applied to capture item relations ...

In recent years, knowledge graph (KG) has attracted significant attention from academia and industry, resulting in the development of numerous technologies for KG construction, completion, and application. XLORE is one of the largest multilingual KGs ...

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn the recommendation policy. The ...

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism, which has been demonstrated effective, is the most fundamental part of GNNs. Although ...

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network ...

Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-...

Reinforcement learning serves as a potent tool for modeling dynamic user interests within recommender systems, garnering increasing research attention of late. However, a significant drawback persists: its poor data efficiency, stemming from its ...

Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top-\(k\) retrieval in Information Retrieval. This duality exists because sparse and ...

Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia and it remains challenging due to highly dynamic ...

Existing sequential Point of Interest (POI) recommendation methods overlook a fact that each city exhibits distinct characteristics and totally ignore the city signature. In this study, we claim that city matters in sequential POI recommendation and fully ...

Contrastive learning has recently emerged as an effective strategy for improving the performance of sequential recommendation. However, traditional models commonly construct the contrastive loss by directly optimizing human-designed positive and negative ...

In recent years, various dense retrieval methods have been developed to improve the performance of search engines with a vectorized index. However, these approaches require a large pre-computed index and have a limited capacity to memorize all semantics ...

Visually-aware recommender systems have found widespread applications in domains where visual elements significantly contribute to the inference of users’ potential preferences. While the incorporation of visual information holds the promise of enhancing ...

In this work, we present TriMLP as a foundational MLP-like architecture for the sequential recommendation, simultaneously achieving computational efficiency and promising performance. First, we empirically study the incompatibility between existing purely ...

Implicit feedback (e.g., user clicks) is widely used in building recommender systems (RS). However, the inherent notorious exposure bias significantly affects recommendation performance. Exposure bias refers a phenomenon that implicit feedback is ...

A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g., like, purchase,...

Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Existing methods for automated patent classification primarily focus on analyzing the text descriptions of patents. However, apart from the ...

Large-scale pretrained language models have achieved compelling performance in a wide range of language understanding and information retrieval tasks. While their large scales ensure capacity, they also hinder deployment. Knowledge distillation offers an ...

In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the ...

Benefiting many information retrieval applications, named entity recognition (NER) has shown impressive progress. Recently, there has been a growing trend to decompose complex NER tasks into two subtasks (e.g., entity span detection (ESD) and entity type ...

Traditional search result evaluation metrics in information retrieval, such as MAP and NDCG, naively focus on topical relevance between a document and search topic and assume this relationship as mono-dimensional and often binary. They neglect document ...

We consider multilingual dialogue systems and ask how the performance of a dialogue system can be improved by using information that is available in other languages than the language in which a conversation is being conducted. We adopt a collaborative ...

Learning to rank models are broadly applied in ad hoc retrieval for scoring and sorting documents based on their relevance to textual queries. The generalizability of the trained model in the learning to rank approach, however, can have an impact on the ...

Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-...