ACM Transactions on Information Systems

Graph classification is an important problem with applications across many domains, for which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. In the literature, to adopt GNNs for the graph classification task, there are two groups ...

During the information search and retrieval (ISR) process, user-system interactions such as submitting queries, examining results, and engaging with information impose some degree of demand on the user’s resources. Within ISR, these demands are well ...

Personalized news recommendation aims to help users find news content they prefer, which has attracted increasing attention recently. There are two core issues in news recommendation: learning news representation and matching candidate news with user ...

A valuable small subset strategically selected from massive online reviews is beneficial to improve consumers’ decision-making efficiency in e-commerce. Existing review selection methods primarily concentrate on the informativeness of reviews and aim to ...

When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted ...

The bursting of media sharing platforms like TikTok, YouTube, and Kwai enables normal users to create and share content with worldwide audiences. The most popular YouTuber can attract up to 100 million followers. Since there are multiple popular platforms,...

Team formation is concerned with the identification of a group of experts who have a high likelihood of effectively collaborating with each other to satisfy a collection of input skills. Solutions to this task have mainly adopted graph operations and at ...

Knowledge Tracing (KT) is the task of modeling a learner’s knowledge state to predict future performance in e-learning systems based on past performance. Deep learning-based methods, such as recurrent neural networks, memory-augmented neural networks, and ...

The recommendation performance of deep collaborative filtering models drops sharply under imperceptible adversarial perturbations. Some methods promote the robustness of recommendation systems by adversarial training. However, these methods only study ...

Multi-behavior recommendation exploits multiple types of user-item interactions, such as view and cart, to learn user preferences and has demonstrated to be an effective solution to alleviate the data sparsity problem faced by the traditional models that ...

We present a simple and versatile framework for evaluating ranked lists in terms of Group Fairness and Relevance, in which the groups (i.e., possible attribute values) can be either nominal or ordinal in nature. First, we demonstrate that when our ...

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on learning ...

Sentiment and emotion, which correspond to long-term and short-lived human feelings, are closely linked to each other, leading to the fact that sentiment analysis and emotion recognition are also two interdependent tasks in natural language processing (...

While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. e.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less ...

Current study on next point-of-interest (POI) recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-...

Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it ...

Task-oriented dialogue systems (TDSs) are assessed mainly in an offline setting or through human evaluation. The evaluation is often limited to single-turn or is very time-intensive. As an alternative, user simulators that mimic user behavior allow us to ...

Knowledge graphs (KGs) can help enhance recommendations, especially for the data-sparsity scenarios with limited user-item interaction data. Due to the strong power of representation learning of graph neural networks (GNNs), recent works of KG-based ...

Measurement of the effectiveness of search engines is often based on use of relevance judgments. It is well known that judgments can be inconsistent between judges, leading to discrepancies that potentially affect not only scores but also system ...

We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In particular, we examine fusion by a convex combination of lexical and semantic ...

To scale the size of Information Retrieval collections, crowdsourcing has become a common way to collect relevance judgments at scale. Crowdsourcing experiments usually employ 100–10,000 workers, but such a number is often decided in a heuristic way. The ...

Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, ...

The present study leverages a recent opportunity we had to create a new English web search test collection for the NTCIR-16 We Want Web (WWW-4) task, which concluded in June 2022. More specifically, through the test collection construction effort, we ...

Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of ...

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute–value pairs, but overlook that different application scenarios may require texts of different styles. Inspired ...

Node representation learning methods, such as graph neural networks, show promising results when testing and training graph data come from the same distribution. However, the existing approaches fail to generalize under distribution shifts when the nodes ...

Job interviews are the most widely accepted method for companies to select suitable candidates, and a critical challenge is finding the right questions to ask job candidates. Moreover, there is a lack of integrated tools for automatically generating ...

Conversational recommender system (CRS) enhances the recommender system by acquiring the latest user preference through dialogues, where an agent needs to decide “whether to ask or recommend”, “which attributes to ask”, and “which items to recommend” in ...

State-of-the-art industrial-level recommender system applications mostly adopt complicated model structures such as deep neural networks. While this helps with the model performance, the lack of system explainability caused by these nearly blackbox models ...

Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users’ multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types ...