ACM Transactions on Information Systems

Reasoning on knowledge graph (KG) has been studied for explainable recommendation due to its ability of providing explicit explanations. However, current KG-based explainable recommendation methods unfortunately ignore the temporal information (such as ...

Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking models that can generalize well on new data. There has been less attention ...

In most E-commerce platforms, whether the displayed items trigger the user’s interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm ...

The cold-start issue is a fundamental challenge in Recommender Systems. The recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model, PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and has shown great ...

In recommender systems, top-N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top-N recommendation, there are increasing concerns on appropriate ...

This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative ...

Search result diversification aims to generate diversified search results so as to meet the various information needs of users. Most of those existing diversification methods greedily select the optimal documents one-by-one comparing with the selected ...

Deep cross-modal retrieval techniques have recently achieved remarkable performance, which also poses severe threats to data privacy potentially. Nowadays, enormous user-generated contents that convey personal information are released and shared on the ...

Funnelling (Fun) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL). In this ensemble method, 1st-tier classifiers, each working on a different and ...

Event representation targets to model the event-reasoning process as a machine-readable format. Previous studies on event representation mostly concentrate on a sole modeling perspective and have not well investigated the scenario-level knowledge, which ...

Query performance prediction (QPP) methods, which aim to predict the performance of a query, often rely on evidences in the form of different characteristic patterns in the distribution of Retrieval Status Values (RSVs). However, for neural IR models, it ...

In the social network, each user has attributes for self-description called user attributes, which are semantically hierarchical. Attribute inference has become an essential way for social platforms to realize user classifications and targeted ...

With the implementation of privacy protection laws such as GDPR, it is increasingly difficult for organizations to legally collect users’ data. However, a typical machine learning-based recommendation algorithm requires the data to learn users’ ...

Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference facing the impatience of human users. Existing work increases inference speed by designing non-autoregressive models for single-turn ...

Traditional recommendation systems are faced with two long-standing obstacles, namely data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information ...

Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have achieved very appealing performance. However, most GNN-based recommendation methods suffer from the problem of data sparsity in practice. Meanwhile, pre-training ...

Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from ...

The generation of large amounts of personal data provides data centers with sufficient resources to mine idiosyncrasy from private records. User modeling has long been a fundamental task with the goal of capturing the latent characteristics of users from ...

Nowadays, most online services are hosted on multi-stakeholder marketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, however, mainly focus on maximizing consumers’ satisfaction by recommending the ...

As recommender systems become increasingly important in daily human decision-making, users are demanding convincing explanations to understand why they get the specific recommendation results. Although a number of explainable recommender systems have ...

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as ...

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world ...